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List of Citations from Science Citation Index for
T. F. Cootes, C. J. Taylor, D. H. Cooper, et al., "
Active Shape Models - Their Training and Application,"
Computer Vision and Image Understanding, 61(1): 38-59,
January 1995.
1995: 1 1996: 10 1997: 32 1998: 19 1999: 23 2000: 34 2001: 21 2002: 1
Total citations: 141
As of 28 Jan 2002
By Year - By Citations - By Year with Abstract
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1995 |
- SOZOU, PD, COOTES, TF, TAYLOR, CJ, and DIMAURO, EC, "NONLINEAR GENERALIZATION OF POINT DISTRIBUTION MODELS USING POLYNOMIAL REGRESSION," IMAGE AND VISION COMPUTING, vol. 13, pp. 451-457, 1995.
Abstract:
We have previously described how to model shape variability by
means of point distribution models (PDM) in which there is a
linear relationship between a set of shape parameters and the
positions of points on the shape. This linear formulation can
fail for shapes which articulate or bend. We show examples of
such failure for both real and synthetic classes of shape. A
new, more general formulation for PDMs, based on polynomial
regression, is presented. The resulting polynomial regression
PDMs (PRPDM) perform well on the data for which the linear
method failed.
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1996 |
- Denzler, J, and Niemann, H, "3D data driven prediction for active contour models based on geometric bounding volumes," PATTERN RECOGNITION LETTERS, vol. 17, pp. 1171-1178, 1996.
Abstract:
Active contour models have proven to be a promising approach
for data driven object tracking without knowledge about the
problem domain and the object. Problems arise in case of fast
moving objects and in natural scenes with heterogeneous
background. In these cases, a prediction step is an essential
part of the tracking mechanism. In this paper we describe a new
approach for modelling the contour of moving objects in the 3D
world. The key point is the description of moving objects by a
simplified geometric model, the sc-called bounding volume. The
contour of moving objects is predicted by estimating the
movement and the shape of the bounding volume in the 3D world
and by projecting its contour to the image plane. Stochastic
optimization algorithms are used to estimate shape parameters
of the bounding volume. The 2D contour of the bounding volume
is used to initialize the active contour, which then extracts
the contour of the moving object. Thus, an update of the motion
and model parameters of the bounding volume is possible. No
task specific knowledge and no a priori knowledge about the
object is necessary. We will show that in the case of convex
polyhedral bounding volumes, this method can be applied to
real-time closed-loop object tracking on standard Unix
workstations. Furthermore, we present experiments which prove
that the robustness for tracking moving objects in front of a
heterogeneous background can be improved.
- Girard, S, Dinten, JM, and Chalmond, B, "Building and training radiographic models for flexible object identification from incomplete data," IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, vol. 143, pp. 257-264, 1996.
Abstract:
The authors address the problem of identifying the projection
of an object from incomplete data extracted from its
radiographic image. They assume that the unknown object is a
particular sample of a flexible object. Their approach consists
first in designing a deformation model able to represent and to
simulate a great variety of samples of the flexible object
radiographic projection. This modellisation is achieved using a
training set of complete data. Then, given the incomplete data,
the identification task consists in estimating the observed
object using the deformation model. The proposed modelling
extracts from the training set, not only the deformation modes,
but also other relevant information (such as probability
distributions on the deformations, relations between
deformations) to use it to regularise the identification step.
- Abrantes, AJ, and Marques, JS, "Class of constrained clustering algorithms for object boundary extraction," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 5, pp. 1507-1521, 1996.
Abstract:
Boundary extraction is a key task in many image analysis
operations. This paper describes a class of constrained
clustering algorithms for object boundary extraction that
includes several well-known algorithms proposed in different
fields (deformable models, constrained clustering, data
ordering, and traveling salesman problems), The algorithms
belonging to this class are obtained by the minimization of a
cost function with two terms: a quadratic regularization term
and an image-dependent term defined by a set of weighting
functions, The minimization of the cost function is achieved by
lowpass filtering the previous model shape and by attracting
the model units toward the centroids of their attraction
regions, To define a new algorithm belonging to this class, the
user has to specify a regularization matrix and a set of
weighting functions that control the attraction of the model
units toward the data, The usefulness of this approach is
twofold: It provides a unified framework for many existing
algorithms in pattern recognition and deformable models, and
allows the design of new recursive schemes.
- Nastar, C, and Ayache, N, "Frequency-based nonrigid motion analysis: Application to four dimensional medical images," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 18, pp. 1067-1079, 1996.
Abstract:
We present a method for nonrigid motion analysis in time
sequences of volume images (4D data). In this method, nonrigid
motion of the deforming object contour is dynamically
approximated by a physically-based deformable surface. In order
to reduce the number of parameters describing the deformation,
we make use of a modal analysis which provides a spatial
smoothing of the surface. The deformation spectrum, which
outlines the main excited modes, can be efficiently used for
deformation comparison. Fourier analysis on time signals of the
main deformation spectrum components provides a temporal
smoothing of the data. Thus a complex nonrigid deformation is
described by only a few parameters: the main excited modes and
the main Fourier harmonics. Therefore, 4D data can be analyzed
in a very concise manner. The power and robustness of the
approach is illustrated by various results on medical data. We
believe that our method has important applications in automatic
diagnosis of heart diseases and in motion compression.
- Chakraborty, A, Staib, LH, and Duncan, JS, "Deformable boundary finding in medical images by integrating gradient and region information," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 15, pp. 859-870, 1996.
Abstract:
Accurately segmenting and quantifying structures is a key issue
in biomedical image analysis. The two conventional methods of
image segmentation, region-based segmentation, and boundary
finding, often suffer from a variety of limitations. Here we
propose a method which endeavors to integrate the two
approaches in an effort to form a unified approach that is
robust to noise and poor initialization. Our approach uses
Green's theorem to derive the boundary of a homogeneous region-
classified area in the image and integrates this with a gray
level gradient-based boundary finder. This combines the
perceptual notions of edge/shape information with gray level
homogeneity. A number of experiments were performed both on
synthetic and real medical images of the brain and heart to
evaluate the new approach, and it is shown that the integrated
method typically performs better when compared to conventional
gradient-based deformable boundary finding. Further, this
method yields these improvements with little increase in
computational overhead, an advantage derived from the
application of the Green's theorem.
- Mitiche, A, and Bouthemy, P, "Computation and analysis of image motion: A synopsis of current problems and methods," INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 19, pp. 29-55, 1996.
Abstract:
The goal of this paper is to offer a structured synopsis of the
problems in image motion computation and analysis, and of the
methods proposed, exposing the underlying models and supporting
assumptions. A sufficient number of pointers to the literature
will be given, concentrating mostly on recent contributions.
Emphasis will be on the detection, measurement and segmentation
of image motion. Tracking, and deformable motion isssues will
be also addressed. Finally, a number of related questions which
could require more investigations will be presented.
- Subsol, G, Thirion, JP, and Ayache, N, "Application of an automatically built 3D morphometric brain atlas: Study of cerebral ventricle shape," VISUALIZATION IN BIOMEDICAL COMPUTING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1131, pp. 373-382, 1996.
Abstract:
In this paper we present new results on the automatic building
of a 3D morphometric brain atlas from volumetric MRI images and
its application to the study of the shape of cerebral
structures. In particular, we show how it is possible to define
''abnormal'' deformations of the cerebral ventricles with a
small set of parameters.
- Hill, A, Cootes, TF, and Taylor, CJ, "Active shape models and the shape approximation problem," IMAGE AND VISION COMPUTING, vol. 14, pp. 601-607, 1996.
Abstract:
Active Shape Models (ASM) use an iterative algorithm to match
statistically defined models of known but variable objects to
instances in images. Each iteration of ASM search involves two
steps: image data interrogation and shape approximation. Here
we consider the shape approximation step in detail. We present
a new method of shape approximation which uses directional
constraints. We show how the error term for the shape
approximation problem can be extended to cope with directional
constraints, and present iterative solutions to the 2D and 3D
problems. We also present an efficient algorithm for the 2D
problem in which a modification of the error term permits a
closed-form approximate solution which can be used to produce
starting estimates for the iterative solution.
- Cootes, TF, DiMauro, EC, Taylor, CJ, and Lanitis, A, "Flexible 3D models from uncalibrated cameras," IMAGE AND VISION COMPUTING, vol. 14, pp. 581-587, 1996.
Abstract:
We describe how to build statistically-based flexible models of
the 3D structure of variable objects, given a training set of
uncalibrated images. We assume that for each example object
there are two labelled images taken from different viewpoints.
From each image pair a 3D structure can be reconstructed, up to
either an affine or projective transformation, depending on
which camera model is used. The reconstructions are aligned by
choosing the transformations which minimise the distances
between matched points across the training set. A statistical
analysis results in an estimate of the mean structure of the
training examples and a compact parameterised model of the
variability in shape across the training set. Experiments have
been performed using pinhole and affine camera models. Results
are presented for both synthetic data and real images.
- Christensen, GE, Rabbitt, RD, and Miller, MI, "Deformable templates using large deformation kinematics," IEEE TRANSACTIONS ON IMAGE PROCESSING, vol. 5, pp. 1435-1447, 1996.
Abstract:
A general automatic approach is presented for accommodating
local shape variation when mapping a two-dimensional (2-D) or
three-dimensional (3-D) template image into alignment with a
topologically similar target image, Local shape variability is
accommodated by applying a vector-field transformation to the
underlying material coordinate system of the template while
constraining the transformation to be smooth (globally positive
definite Jacobian), Smoothness is guaranteed without
specifically penalizing large-magnitude deformations of small
subvolumes by constraining the transformation on the basis of a
Stokesian limit of the fluid-dynamical Navier-Stokes equations,
This differs fundamentally from quadratic penalty methods, such
as those based on linearized elasticity or thin-plate splines,
in that stress restraining the motion relaxes over time
allowing large-magnitude deformations, Kinematic nonlinearities
are inherently necessary to maintain continuity of structures
during large-magnitude deformations, and are included in all
results, After initial global registration, final mappings are
obtained by numerically solving a set of nonlinear partial
differential equations associated with the constrained
optimization problem, Automatic regridding is performed by
propagating templates as the nonlinear transformations
evaluated on a finite lattice become singular, Application of
the method to intersubject registration of neuroanatomical
structures illustrates the ability to account for local
anatomical variability.
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1997 |
- Luettin, J, and Thacker, NA, "Speechreading using probabilistic models," COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 65, pp. 163-178, 1997.
Abstract:
We describe a robust method for locating and tracking lips in
gray-level image sequences. Our approach learns patterns of
shape variability from a training set which constrains the
model during image search to only deform in ways similar to the
training examples, Image search is guided by a learned gray-
level model which is used to describe the large appearance
variability of lips, Such variability might be due to different
individuals, illumination, mouth opening, specularity, or
visibility of teeth and tongue, Visual speech features are
recovered from the tracking results and represent both shape
and intensity information, We describe a speechreading (lip-
reading) system, where the extracted features are modeled by
Gaussian distributions and their temporal dependencies by
hidden Markov models. Experimental results are presented for
locating lips, tracking lips, and speechreading. The database
used consists of a broad variety of speakers and was recorded
in a natural environment with no special lighting or lip
markers used, For a speaker independent digit recognition task
using visual information only, the system achieved an accuracy
about equivalent to that of untrained humans. (C) 1997 Academic
Press.
- Solloway, S, Hutchinson, CE, Waterton, JC, and Taylor, CJ, "The use of active shape models for making thickness measurements of articular cartilage from MR images," MAGNETIC RESONANCE IN MEDICINE, vol. 37, pp. 943-952, 1997.
Abstract:
Previously reported studies to quantify articular cartilage
have used labor-intensive manual or semi-automatic data-driven
techniques, demonstrating high accuracy and precision. However,
none has been able to automate the segmentation process. This
paper describes a fast, automatic, model-based approach to
segmentation and thickness measurement of the femoral cartilage
in 3D T-1-weighted images using active shape models (ASMs).
Systematic experiments were performed to assess the accuracy
and precision of the technique with in vivo images of both
normal and abnormal knees. Segmentation accuracy was determined
by comparing the results of the segmentation with the
boundaries delineated by a radiologist, The mean error in
locating the boundary was 0.57 pixels. To assess the precision
of the measurement technique, the mean thickness of the femoral
cartilage was calculated for repeated scans of five healthy
volunteers. A mean coefficient of variation (CV) of 2.8% was
obtained for the thickness measurements.
- Redhead, AL, Kotcheff, ACW, Taylor, CJ, Porter, ML, and Hukins, DWL, "An automated method for assessing routine radiographs of patients with total hip replacements," PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H- JOURNAL OF ENGINEERING IN MEDICINE, vol. 211, pp. 145-154, 1997.
Abstract:
This paper describes a new, fully automated method of locating
objects on radiographs of patients with total joint
replacements (TJRs). A statistical computer model, known as an
active shape model, was trained to identify the position of the
femur, pelvis, stem and cup marker wire on radiographs of
patients with Charnley total hip prostheses. Once trained, the
model was able to locate these objects through a process of
automatic image searching, despite their appearance depending
on the orientation and anatomy of the patient. Experiments were
carried out to test the accuracy with which the model was able
to fit to previously unseen data and with which reference
points could be calculated from the model points. The model was
able to locate the femur and stem with a mean error of
approximately 0.8 mm and a 95 per cent confidence limit of 1.7
mn. Once the model had successfully located these objects, the
midpoint of the stem head could be calculated with a mean error
of approximately 0.2 mm. Although the model has been trained on
Charnley total hip replacements, the method is generic and so
can be applied to radiographs of patients with any TJR. This
paper shows that computer models can form the basis of a quick,
automatic method of taking measurements from standard clinical
radiographs.
- Delibasis, K, Undrill, PE, and Cameron, GG, "Designing Fourier descriptor-based geometric models for object interpretation in medical images using genetic algorithms," COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 66, pp. 286-300, 1997.
Abstract:
In previous work we have modeled simple 3D anatomical objects
using deformed superquadrics and established their optimal
position with the aid of genetic algorithms (GAs). Here we
extend the complexity of the search object using 3D Fourier
descriptor (FD) representations and allow GAs once again to
optimize the object's shape and position. Using magnetic
resonance image data, we perform an approximate segmentation on
one lateral ventricle in the brain and use the FDs from this as
seeding values for the GAs to search for the left and right
lateral ventricles in seven 3D data sets. We show that the
method is capable of coping with normal biological variation.
Finally, we compare FD/GA-guided segmentation with a manually
guided interactive region growing method and find an agreement
of 78 +/- 10% in voxel classification with a corresponding
average edge placement error of 2.2 +/- 0.4 mm. (C) 1997
Academic Press.
- Smyth, PP, Taylor, CJ, and Adams, JE, "Automatic measurement of vertebral shape using Active Shape Models," INFORMATION PROCESSING IN MEDICAL IMAGING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1230, pp. 441-446, 1997.
Abstract:
In this paper, we describe how Active Shape Models (ASMs) have
been used to accurately and robustly locate vertebrae in noisy
lateral Dual Energy X-ray Absorptiometry (DXA) images of the
spine. Vertebrae were located using either separate models for
each vertebra, or a combined model of the whole spine. The
combined model was found to be more robust. We show that ASMs
allow normal vertebrae to be located as accurately as by human
operators. We measure the performance of ASMs in locating
fractured vertebrae of osteoporotic patients. We also describe
how model parameters may be used to estimate how accurately a
vertebra had been located, in order to detect vertebrae for
which search had failed.
- Joshi, SC, Banerjee, A, Christensen, GE, Csernansky, JG, Haller, JW, Miller, MI, and Wang, L, "Gaussian random fields on sub-manifolds for characterizing brain surfaces," INFORMATION PROCESSING IN MEDICAL IMAGING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1230, pp. 381-386, 1997.
Abstract:
This paper provides analytical methods for characterizing the
variation of the shape of neuroanatomically significant
substructures of the brain in an ensemble of brain images. The
focus of this paper is on the neuro-anatomical variation of the
"shape" of 2-dimensional surfaces in the brain. Brain surfaces
are studied by building templates that are smooth sub-manifolds
of the underlying coordinate system of the brain. Variation of
the shape in populations is quantified via defining Gaussian
random vector fields on these sub-manifolds Methods for the
empirical construction of Gaussian random vector fields for
representing the variations of the substructures are presented.
As an example, using these methods we characterize the shape of
the hippocampus in a population of normal controls and
schizophrenic brains. Results from a recently completed study
comparing shapes of the hippocampus in a group of matched
schizophrenic and normal control subjects are presented.
Bayesian hypothesis test is formulated to cluster the normal
and schizophrenic hippocampi in the population of 20
individuals.
- Duta, N, and Sonka, M, "Segmentation and interpretation of MR brain images using an improved knowledge-based active shape model," INFORMATION PROCESSING IN MEDICAL IMAGING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1230, pp. 375-380, 1997.
Abstract:
An improvement of the Active Shape procedure introduced by
Cootes and Taylor is presented. The new automated brain
segmentation and interpretation approach incorporates a priori
knowledge about neuroanatomic structures and their specific
structural relationships to provide robust segmentation and
labeling. The method was trained in 8 MR brain images and
tested in 19 brain images by comparison to observer-defined
independent standards. Neuroanatomic structures in all images
from the test set were successfully identified. The presented
method is applicable to virtually any task involving deformable
shape analysis.
- Ahmad, T, Taylor, CJ, Lanitis, A, and Cootes, TF, "Tracking and recognising hand gestures, using statistical shape models," IMAGE AND VISION COMPUTING, vol. 15, pp. 345-352, 1997.
Abstract:
Hand gesture recognition from video images is of considerable
interest as a means of providing simple and intuitive man-
machine interfaces. Possible applications range from replacing
the mouse as a pointing device to virtual reality and
communication with the deaf. We describe an approach to
tracking a hand in an image sequence and recognising, in each
video frame. which of five gestures it has adopted. A
statistically based Point Distribution Model (PDM) is used to
provide a compact parametrised description of the shape of the
hand for any of the gestures or the transitions between them.
The values of the resulting shape parameters are used in a
statistical classifier to identify gestures. The model can be
used as a deformable template to track a hand through a video
sequence but this proves unreliable. We describe how a set of
models, one for each of the five gestures, can be used for
tracking with the appropriate model selected automatically. We
show that this results in reliable tracking and gesture
recognition for two 'unseen' video sequences in which all the
gestures are used.
- Sozou, PD, Cootes, TF, Taylor, CJ, DiMauro, EC, and Lanitis, A, "Non-linear point distribution modelling using a multi-layer perceptron," IMAGE AND VISION COMPUTING, vol. 15, pp. 457-463, 1997.
Abstract:
Objects of the same class sometimes exhibit variation in shape.
This shape variation has previously been modelled by means of
point distribution models (PDMs) in which there is a linear
relationship between a set of shape parameters and the
positions of points on the shape. A polynomial regression
generalization of PDMs, which succeeds in capturing certain
forms of non-linear shape variability, has also been described.
Here we present a new form of PDM, which uses a multi-layer
perceptron to carry out non-linear principal component
analysis. We compare the performance of the new model with that
of the existing models on two classes of variable shape: one
exhibits bending, and the other exhibits complete rotation. The
linear PDM fails on both classes of shape; the polynomial
regression model succeeds for the first class of shapes but
fails for the second; the new multi-layer perceptron model
performs well for both classes of shape. The new model is the
most general formulation for PDMs which has been proposed to
date. (C) 1997 Elsevier Science B.V.
- Smyth, PP, Taylor, CJ, and Adams, JE, "Automatic measurement of vertebral shape using active shape models," IMAGE AND VISION COMPUTING, vol. 15, pp. 575-581, 1997.
Abstract:
In this paper, we describe how Active Shape Models (ASMs) have
been used to accurately and robustly locate vertebrae in
lateral Dual Energy X-ray Absorptiometry (DXA) images of the
spine. DXA images are of low spatial resolution, and contain
significant random and structural noise, providing a difficult
challenge for object location methods. All vertebrae in the
image were searched for simultaneously, improving robustness in
location of individual vertebrae by making use of constraints
on shape provided by the position of other vertebrae. We show
that the use of ASMs with minimal user interaction allows
accuracy to be obtained which is as good as that achievable by
human operators, as well as high precision. Having located each
vertebra, it is desirable to evaluate whether it has been
located sufficiently accurately for shape measurements to be
useful. We determined this on the basis of grey-level model
fit, which was shown to usefully detect poorly located
vertebrae, which should enable accuracy to be improved by
rejecting proposed search solutions whose grey-level fit was
poorer than a threshold. (C) 1997 Elsevier Science B.V.
- Kotcheff, ACW, and Taylor, CJ, "Automatic construction of eigenshape models by genetic algorithm," INFORMATION PROCESSING IN MEDICAL IMAGING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1230, pp. 1-14, 1997.
Abstract:
A new approach to the problem of automatic construction of
eigenshape models is presented. Eigenshape models have proved
to be successful in a variety of medical image analysis
problems. However, automatic model construction is a difficult
problem, and in many applications the models are built by hand
- a painstaking process. Previous attempts to produce models
automatically have been applicable only in specific cases or
under certain assumptions. We show that the problem can be
understood very simply in terms of shape symmetries. The pose
and parameterisation of each shape must be chosen so as to
produce a model that is compact and specific. Mie define an
objective function that measures these properties. The problem
of automatic model construction is thus reduced to an
optimisation problem. We show that the objective function we
define can be optimised by a Genetic Algorithm, and produces
models that are better than hand built ones.
- Rueckert, D, and Burger, P, "Geometrically deformable templates for shape-based segmentation and tracking in cardiac MR images," ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1223, pp. 83-98, 1997.
Abstract:
We present a new approach to shape-based segmentation and
tracking of multiple, deformable anatomical structures in
cardiac MR images. We propose to use an energy-minimizing
geometrically deformable template (GDT) which can deform into
similar shapes under the influence of image forces. The degree
of deformation of the template from its equilibrium shape is
measured by a penalty function associated with mapping between
the two shapes. In 2D, this term corresponds to the bending
energy of an idealized thin-plate of metal. By minimizing this
term along with the image energy terms of the classic
deformable model, the deformable template is attracted towards
objects in the image whose shape is similar to its equilibrium
shape. This framework allows for the simultaneous segmentation
of multiple deformable objects using intra- as well as inter-
shape information. The energy minimization problem of the
deformable template is formulated in a Bayesian framework and
solved using relaxation techniques: Simulated Annealing (SA), a
stochastic relaxation technique is used for segmentation while
Iterated Conditional Modes (ICM), a deterministic relaxation
technique is used for tracking. We present results of the
algorithm applied to the reconstruction of the left and right
ventricle of the human heart in 4D MR images.
- Liu, TL, and Geiger, D, "Visual deconstruction: Recognizing articulated objects," ENERGY MINIMIZATION METHODS IN COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1223, pp. 295-309, 1997.
Abstract:
We propose a deconstruction framework to recognize and find
articulated objects. In particular we axe interested in human
arm and leg articulations. The deconstruction view of
recognition naturally decomposes the problem of finding an
object in an image, into the one of (i) extracting key features
in an image, (ii) detecting key points in the models, (iii)
segmenting an image, and (iv) comparing shapes. All of these
subproblems can not be resolved independently. Together, they
reconstruct the object in the image. We briefly address (i) and
(ii) to focus on solving together shape similarity and
segmentation, combining top-down & bottom-up algorithms. We
show that the visual deconstruction approach is derived as an
optimization for a Bayesian-Information theory, and that the
whole process is naturally generated by the guaranteed Dijkstra
optimization algorithm.
- Lanitis, A, Taylor, CJ, and Cootes, TF, "Automatic interpretation and coding of face images using flexible models," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 19, pp. 743-756, 1997.
Abstract:
Face images are difficult to interpret because they are highly
variable. Sources of variability include individual appearance,
3D pose, facial expression, and lighting. We describe a compact
parametrized model of facial appearance which takes into
account all these sources of variability. The model represents
both shape and gray-level appearance, and is created by
performing a statistical analysis over a training set of face
images. A robust multiresolution search algorithm is used to
fit the model to faces in new images. This allows the main
facial features to be located, and a set of shape, and gray-
level appearance parameters to be recovered. A good
approximation to a given face can be reconstructed using less
than 100 of these parameters. This representation can be used
for tasks such as image coding, person identification, 3D pose
recovery, gender recognition, and expression recognition.
Experimental results are presented for a database of 690 face
images obtained under widely varying conditions of 3D pose,
lighting, and facial expression. The system performs well on
ail the tasks listed above.
- Vetter, T, and Poggio, T, "Linear object classes and image synthesis from a single example image," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 19, pp. 733-742, 1997.
Abstract:
The need to generate new views of a 3D object from a single
real image arises in several fields, including graphics and
object recognition. While the traditional approach relies on
the use of 3D models, we have recently introduced [1], [2], [3]
simpler techniques that are applicable under restricted
conditions. The approach exploits image transformations that
are specific to the relevant object class, and learnable from
example Views of other ''prototypical'' objects df the same
class. In this paper, we introduce such a technique by
extending the notion of linear class proposed by Poggio and
Vetter. For linear object classes, it is shown that linear
transformations can be learned exactly from a basis set of 2D
prototypical views. We demonstrate the approach on artificial
objects and then show preliminary evidence that the technique
can effectively ''rotate'' high-resolution face images from a
single 2D view.
- Pavlovic, VI, Sharma, R, and Huang, TS, "Visual interpretation of hand gestures for human-computer interaction: A review," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 19, pp. 677-695, 1997.
Abstract:
The use of hand gestures provides an attractive alternative to
cumbersome interface devices for human-computer interaction
(HCl). In particular, Visual interpretation of hand gestures
can help in achieving the ease and naturalness desired for HCl.
This has motivated a very active research area concerned with
computer vision-based analysis and interpretation of hand
gestures. We survey the literature on visual interpretation of
hand gestures in the context of its role in HCl. This
discussion is organized on the basis of the method used for
modeling, analyzing, and recognizing gestures. Important
differences in the gesture interpretation approaches arise
depending on whether a 3D model of the human hand or an image
appearance model of the human hand is used. 3D hand models
offer a way of more elaborate modeling of hand gestures but
lead to computational hurdles that have not been overcome given
the real-time requirements of HCl. Appearance-based models lead
to computationally efficient ''purposive'' approaches that work
well under constrained situations but seem to lack the
generality desirable for HCl. We also discuss implemented
gestural systems as well as other potential applications of
vision-based gesture recognition. Although the current progress
is encouraging, further theoretical as well as computational
advances are needed before gestures can be widely used for HCl.
We discuss directions of future research in gesture
recognition, including its integration with other natural modes
of human-computer interaction.
- Yow, KC, and Cipolla, R, "Feature-based human face detection," IMAGE AND VISION COMPUTING, vol. 15, pp. 713-735, 1997.
Abstract:
Human face detection has always been an important problem for
face, expression and gesture recognition. Though numerous
attempts have been made to detect and localize faces, these
approaches have made assumptions that restrict their extension
to more general cases. We identify that the key factor in a
generic and robust system is that of using a large amount of
image evidence, related and reinforced by model knowledge
through a probabilistic framework. In this paper, we propose a
feature-based algorithm for detecting faces that is
sufficiently generic and is also easily extensible to cope with
more demanding variations of the imaging conditions. The
algorithm detects feature points from the image using spatial
filters and groups them into face candidates using geometric
and gray level constraints. A probabilistic framework is then
used to reinforce probabilities and to evaluate the likelihood
of the candidate as a face. We provide results to support the
validity of the approach and demonstrate its capability to
detect faces under different scale, orientation and viewpoint.
(C) 1997 Elsevier Science B.V.
- Vetter, T, and Troje, NF, "Separation of texture and shape in images of faces for image coding and synthesis," JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, vol. 14, pp. 2152-2161, 1997.
Abstract:
Human faces differ in shape and texture. Image representations
based on this separation of shape and texture information have
been reported by several authors [for a review, see Science
272, 1905 (1996)]. We investigate such a representation of
human faces based on a separation of texture and two-
dimensional shape information. Texture and shape were separated
by use of pixel-by-pixel correspondence among the various
images, which was established through algorithms known from
optical flow computation. We demonstrate the improvement of the
proposed representation over well-established pixel-based
techniques in terms of coding efficiency and in terms of the
ability to generalize to new images of faces. The evaluation is
performed by calculating different distance measures between
the original image and its reconstruction and by measuring the
time that human subjects need to discriminate them. (C) 1997
Optical Society of America.
- Taylor, CJ, Cootes, TF, Lanitis, A, Edwards, G, Smyth, P, and Kotcheff, ACW, "Model-based interpretation of complex and variable images," PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES, vol. 352, pp. 1267-1274, 1997.
Abstract:
The ultimate goal of machine vision is image understanding-the
ability not only to recover image structure but also to know
what it represents. By definition, this involves the use of
models which describe and label the expected structure of the
world. Over the past decade, model-based vision has been
applied successfully to images of man-made objects. It has
proved much more difficult to develop model-based approaches to
the interpretation of images of complex and variable structures
such as faces or the internal organs of the human body (as
visualized in medical images). In such cases it has been
problematic even to recover image structure reliably without a
model to organize the often noisy and incomplete image
evidence. The key problem is that of variability. To be useful,
a model needs to be specific-that is, to be capable of
representing only 'legal' examples of the modelled object(s).
It has proved difficult to achieve this whilst allowing for
natural variability. Recent developments have overcome this
problem; it has been shown that specific patterns of
variability in shape and grey-level appearance can be captured
by statistical models that can be used directly in image
interpretation. The details of the approach are outlined and
practical examples from medical image interpretation and face
recognition are used to illustrate how previously intractable
problems can now be tackled successfully. It is also
interesting to ask whether these results provide any possible
insights into natural vision; for example, we show that the
apparent changes in shape which result from viewing three-
dimensional objects from different viewpoints can be modelled
quite well in two dimensions; this may lend some support to the
'characteristic views' model of natural vision.
- Subsol, G, Roberts, N, Doran, M, Thirion, JP, and Whitehouse, GH, "Automatic analysis of cerebral atrophy," MAGNETIC RESONANCE IMAGING, vol. 15, pp. 917-927, 1997.
Abstract:
3D MR data obtained for 10 healthy control subjects have been
used to build a brain atlas, The atlas is built in four stages,
First, a set of features that are unambiguously definable and
anatomically relevant need to be computed for each item in the
database, The chosen features are crest lines along which the
maximal principal curvature of the surface of the brain is
maximal in its associated principal direction, Second, a
nonrigid registration algorithm is used to determine the common
crest lines among the subjects in the database, These crest
lines form the structure of the atlas. Third, a set of crest
lines is taken as a reference set and a modal analysis is
performed to determine the fundamental deformations that are
necessary to bring the individual data in line with the
reference set, The deformations are averaged and the set of
mean crest lines becomes the atlas, Finally, the standard
deviation of the deformations between the atlas and the items
in the database defines the normal variation in the relative
positions of the crest lines in a healthy population, In a
fully automatic procedure, the crest lines on the surface of
the brain adjacent to the cerebral ventricles in a patient with
primary progressive aphasia were compared to the atlas;
confirmation that the brain of this patient demonstrates
atrophy was provided by stereological analysis that showed that
the volume of the left cerebral hemisphere is 48.8 ml (CE 2.8%)
less than the volume of the right cerebral hemisphere in the
region of the temporal and frontal lobes, When the amplitude of
the deformations necessary to register the crest lines obtained
for the patient with the atlas were greater than three standard
deviations beyond the variability inherent in the atlas, the
deformation was considered significant, Four of the five main
deformation modes of the longest crest line of the surface of
the brain adjacent to the cerebral ventricles were
significantly different in the patient with primary progressive
aphasia compared to the atlas, The ventricles are
preferentially enlarged in the left cerebral hemisphere,
Furthermore, they are closer together posteriorly and further
apart anteriorly than in the atlas, These observations may be
indicative of the atrophy of the temporal and frontal lobes of
the left cerebral hemisphere noted in the patient, Ultimately,
the approach may provide a useful screening technique for
identifying brain diseases involving cerebral atrophy, Serial
studies of individual patients may provide insights into the
processes controlling or affected by particular diseases. (C)
1997 Elsevier Science Inc.
- Rueckert, D, and Burger, P, "Shape-based segmentation and tracking in 4D cardiac MR images," CVRMED-MRCAS'97, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1205, pp. 43-52, 1997.
Abstract:
We present a new approach to shape-based segmentation and
tracking of multiple, deformable anatomical structures in
cardiac MR images. We propose to use an energy-minimizing
geometrically deformable template (GDT) which can deform into
similar shapes under the influence of image forces. The degree
of deformation of the template from its equilibrium Shape is
measured by a penalty function associated with mapping between
the two shapes. In 2D, this term corresponds to the bending
energy of an idealized thin-plate of metal. By minimizing this
term along with the image energy terms of the classic
deformable model, the deformable template is attracted towards
objects in the image whose shape is similar to its equilibrium
shape. This framework allows the simultaneous segmentation of
multiple deformable objects using intra-as well as inter-shape
information. The energy minimization problem of the deformable
template is formulated in a Bayesian framework and solved using
relaxation techniques: Simulated Annealing (SA), a stochastic
relaxation technique is used for segmentation while Iterated
Conditional Modes (ICM), a deterministic relaxation technique
is used for tracking. We present results of the algorithm
applied to the reconstruction of the left and right ventricle
of the human heart in 4D MR images.
- Montagnat, J, and Delingette, H, "Volumetric medical images segmentation using shape constrained deformable models," CVRMED-MRCAS'97, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1205, pp. 13-22, 1997.
Abstract:
In this paper we address the problem of extracting geometric
models from lour contrast volumetric images, given a template
or reference shape of that model. We proceed by deforming a
reference model in a volumetric image. This reference
deformable model is represented as a simplex mesh submitted to
regularizing shape constraint. Furthermore, we introduce an
original approach that combines the deformable model framework
with the elastic registration (based on iterative closest point
algorithm) method. This new method increases the robustness of
segmentation while allowing very complex deformation, of the
original template. Examples of segmentation of the liver and
brain ventricles are provided.
- Grzeszczuk, RP, and Levin, DN, "''Brownian strings'': Segmenting images with stochastically deformable contours," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 19, pp. 1100-1114, 1997.
Abstract:
This paper describes an image segmentation technique in which
an arbitrarily shaped contour was deformed stochastically until
it fitted around an object of interest. The evolution of the
contour was controlled by a simulated annealing process which
caused the contour to settle into the global minimum of an
image-derived ''energy'' function. The nonparametric energy
function was derived from the statistical properties of
previously segmented images, thereby incorporating prior
experience. Since the method was based on a state space search
for the contour with the best global properties, it was stable
in the presence of image errors which confound segmentation
techniques based on local criteria, such as connectivity.
Unlike ''snakes'' and other active contour approaches, the new
method could handle arbitrarily irregular contours in which
each interpixel crack represented an independent degree of
freedom. Furthermore, since the contour evolved toward the
global minimum of the energy, the method was more suitable for
fully automatic applications than the snake algorithm, which
frequently has to be reinitialized when the contour becomes
trapped in local energy minima. High computational complexity
was avoided by efficiently introducing a random local
perturbation in a time independent of contour length, providing
control over the size of the perturbation, and assuring that
resulting shape changes were unbiased. The method was
illustrated by using it to find the brain surface in magnetic
resonance head images and to track blood vessels in angiograms.
- OToole, AJ, Vetter, T, Volz, H, and Salter, EM, "Three-dimensional caricatures of human heads: distinctiveness and the perception of facial age," PERCEPTION, vol. 26, pp. 719-732, 1997.
Abstract:
A standard facial-caricaturing algorithm was applied to a
three-dimensional representation of human heads. This algorithm
sometimes produced heads that appeared 'caricatured'. More
commonly, however, exaggerating the distinctive three-
dimensional information in a face seemed to produce an increase
in the apparent age of the face-both at a local level, by
exaggerating small facial creases into wrinkles, and at a more
global level via changes that seemed to make the underlying
structure of the skull more evident. Concomitantly, de-emphasis
of the distinctive three-dimensional information in a face made
it appear relatively younger than the veridical and caricatured
faces. More formally, face-age judgments made by human
observers were ordered according to the level of caricature,
with anticaricatures judged younger than veridical faces, and
veridical faces judged younger than caricatured faces. These
results are discussed in terms of the importance of the nature
of the features made more distinct by a caricaturing algorithm
and the nature of human representation(s) of faces.
- Hozumi, T, Yoshida, K, Yoshioka, H, Yagi, T, Akasaka, T, Takagi, T, Nishiura, M, Watanabe, M, and Yoshikawa, J, "Echocardiographic estimation of left ventricular cavity area with a newly developed automated contour tracking method," JOURNAL OF THE AMERICAN SOCIETY OF ECHOCARDIOGRAPHY, vol. 10, pp. 822-829, 1997.
Abstract:
Development of an automated contour tracking method provides
detection and tracking of the endocardial boundary using the
energy minimization method without tracing a region of
interest. The purpose of this study was to compare the
automated contour tracking method and manually drawn methods
for the measurement of left ventricular cavity areas and
fractional area change. Apical four-chamber view was visualized
and recorded for off-line analysis in 11 patients by means of
two-dimensional echocardiography. The automated contour
tracking method automatically traces the endocardial border
from the recorded images and calculates left ventricular cavity
areas (end-diastole and end-systole) and fractional area
change. In the same images selected as end-diastole and end-
systole in the automated contour tracking method, left
ventricular endocardial border was manually traced to calculate
left ventricular cavity areas and fractional area change. Both
methods were compared by Linear regression analysis for the
measurement of cavity areas and fractional area change. Left
ventricular areas measured by the automated contour tracking
method showed an excellent correlation with those by the manual
method (end-diastole: r = 0.99, y = 0.83x + 2.6, standard error
of the estimate = 1.5 cm(2); end-systole: r = 0.99, y = 0.96x -
0.8, standard error of the estimate = 1.2 cm(2)). The mean
differences between the automated contour tracking and manual
methods were -3.1 +/- 5.1 cm(2) and -1.6 +/- 2.4 cm(2) at end-
diastole and end-systole, respectively. Fractional area change
determined by the automated contour tracking method correlated
well with that by the manual method (r = 0.95, y = 1.17x - 6.5,
standard error of the estimate = 3.4%). The mean difference
between the automated contour tracking and manual methods was -
0.8% +/- 7.1%. In conclusion, a newly developed automated
contour tracking method correlates highly with the manual
method for the estimation of left ventricular cavity areas and
fractional area change in high-quality images. This suggests
that this new technique may be useful in the automated
quantitation of left ventricular function in patients with
high-quality images with no dropout and no intercavity artifact
or structure.
- Huang, CL, Chang, WT, Wu, LC, and Wang, JK, "Three-dimensional PET emission scan registration and transmission scan synthesis," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 16, pp. 542-561, 1997.
Abstract:
The duration of a positron emission tomography (PET) imaging
scan can be reduced if the transmission scan of one patient
which is used for emission correction can be synthesized by
using the reference transmission scan of another patient, In
this paper, we propose a new intersubjects PET emission scan
registration method and PET transmission synthesis method by
using the boundary information of the body or brain scan of the
PET emission scans. The PET emission scans have poor image
quality and different intensity statistics so that we
preprocess the emission scans to have similar histogram and
then apply the point distribution model (PDM) [15] to extract
the contours of the emission scan, The extracted boundary
contour of every slice is used to reconstruct the three-
dimensional (3-D) surface of the reference set and the target
set, Our registration is 3-D surface-based which uses the
normal flow method [17] to find the correspondence vector field
between two 3-D reconstructed surfaces, Since it is difficult
to analyze internal organ using the PET emission scan imaging
without correction, we assume that the deformation of internal
organ is homogeneous, With the corresponding vector field
between the two emission scans and the transmission scan of the
reference set, we can synthesize the transmission scan of the
target set.
- Huang, CL, and Huang, YM, "Facial expression recognition using model-based feature extraction and action parameters classification," JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, vol. 8, pp. 278-290, 1997.
Abstract:
This paper introduces an automatic facial expression
recognition system which consists of two parts: facial feature
extraction and facial expression recognition. The system
applies the point distribution model and the gray-level model
to find the facial features. Then the position variations of
certain designated points on the facial feature are described
by 10 action parameters (APs). There are two phases in the
recognition process: the training phase and the recognition
phase. In the training phase, given 90 different expressions,
the system classifies the principal components of the APs of
all training expressions into six different clusters. In the
recognition phase, given a facial image sequence, it identifies
the facial expressions by extracting the 10 APs, analyzes the
principal components, and finally calculates the AP profile
correlation for a higher recognition rate. In the experiments,
our system has demonstrated that it can recognize the facial
expression effectively. (C) 1997 Academic Press.
- Choi, KN, Cross, ADJ, and Hancock, ER, "Localising facial features with matched filters," AUDIO- AND VIDEO-BASED BIOMETRIC PERSON AUTHENTICATION, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1206, pp. 11-20, 1997.
Abstract:
This paper describes a study of facial feature recognition
using matched filter techniques. The basic aim is to develop a
set of filters that can be used to characterise each of eight
different facial features. These are left and right eyes, left
and right-eyebrows, hairline, nose, mouth and chin. The matched
filters are extracted from training images using inverse
Fourier analysis. We provide an experimental evaluation of the
method on the University of Berne face data-base.. Here we
explore the most effective choice of training data so that the
filters can be effectively applied when the facial pose varies.
We also evaluate the effectiveness of the method when facial
occlusion due to spectacles is present.
- Bowden, R, Mitchell, TA, and Sarhadi, M, "Cluster based nonlinear principle component analysis," ELECTRONICS LETTERS, vol. 33, pp. 1858-1859, 1997.
Abstract:
In the field of computer vision, principle component analysis
(PCA) is often used to provide statistical models of shape,
deformation or appearance. This simple statistical model
provides a constrained. compact approach to model based vision.
However, as larger problems are considered. high dimensionality
and nonlinearity make linear PCA an unsuitable and unreliable
approach. A nonlinear PCA (NLPCA) technique is proposed which
uses cluster analysis and dimensional reduction to provide a
fast. robust solution. Simulation results on both 2D contour
models and greyscale images are presented.
- Joshi, SC, Miller, MI, and Grenander, U, "On the geometry and shape of brain sub-manifolds," INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, vol. 11, pp. 1317-1343, 1997.
Abstract:
This paper develops mathematical representations for neuro-
anatomically significant substructures of the brain and their
variability in a population. The focus of the paper is an the
neuro-anatomical variation of the geometry and the "shape" of
two-dimensional surfaces in the brain. As examples, we focus on
the cortical and hippocampal surfaces in an ensemble of Macaque
monkeys and human MRI brains. The "shapes" of the substructures
are quantified via the construction of templates; the
variations are represented by defining probabilistic
deformations of the template. Methods for empirically
estimating probability measures on these deformations are
developed by representing the deformations as Gaussian random
vector fields on the embedded sub-manifolds. The Gaussian
random vector fields are constructed as quadratic mean limits
using complete orthonormal bases on the sub-manifolds. The
complete orthonormal bases are generated using modes of
vibrations of the geometries of the brain sub-manifolds. The
covariances are empirically estimated from an ensemble of brain
data. Principal component analysis is presented for
characterizing the "eigen-shape" of the hippocampus in an
ensemble of MRI-MPRAGE whole brain images. Clustering based on
eigen-shape is presented for two sub-populations of normal and
schizophrenic.
- Christensen, GE, Joshi, SC, and Miller, MI, "Volumetric transformation of brain anatomy," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 16, pp. 864-877, 1997.
Abstract:
This paper presents diffeomorphic transformations of three-
dimensional (3-D) anatomical image data of the macaque
occipital lobe and whole brain cryosection imagery and of deep
brain structures in human brains as imaged via magnetic
resonance imagery, These transformations are generated in a
hierarchical manner, accommodating both global and local
anatomical detail, The initial low-dimensional registration is
accomplished by constraining the transformation to be in a low-
dimensional basis, The basis is defined by the Green's function
of the elasticity operator placed at predefined locations in
the anatomy and the eigenfunctions of the elasticity operator,
The high-dimensional large deformations are vector fields
generated via the mismatch between the template and target-
image volumes constrained to be the solution of a Navier-Stokes
fluid model. As part of this procedure, the Jacobian of the
transformation is tracked, insuring the generation of
diffeomorphisms. It is shown that transformations constrained
by quadratic regularization methods such as the Laplacian,
biharmonic, and linear elasticity models, do not ensure that
the transformation maintains topology and, therefore, must only
be used for coarse global registration.
- Hill, A, Brett, AD, and Taylor, CJ, "Automatic landmark identification using a new method of non- rigid correspondence," INFORMATION PROCESSING IN MEDICAL IMAGING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1230, pp. 483-488, 1997.
Abstract:
A method for corresponding the boundaries of two shapes is
presented. The algorithm locates a matching pair of sparse
polygonal approximations, one for each of a pair of boundaries,
by minimising a cost function using a greedy algorithm. The
cost function expresses the dissimilarity in both the shape and
representation error (with respect to the defining boundary) of
the sparse polygons. Results are presented for three classes of
shape which exhibit various types of non-rigid deformation. The
algorithm is also applied to an automatic landmark
identification task for the construction of statistical shape
models.
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1998 |
- Schuhmann, D, Seemann, M, Schoepf, UJ, Haubner, M, Krapichler, C, Gebicke, K, Reiser, M, and Englmeier, KH, "Computerized diagnostic data analysis and 3-D visualization," RADIOLOGE, vol. 38, pp. 799-809, 1998.
Abstract:
Purpose:To survey methods for 3D data visualization and image
analysis which can be used for computer based diagnostics.
Material and methods: The methods available are explained in
short terms and links to the literature are presented. Methods
which allow basic manipulation of 3D data are windowing,
rotation and clipping. More complex methods for visualization
of 3D data are multiplanar reformation, volume projections
(MIP,semi-transparent projections) and surface projections.
Methods for image analysis comprise local data transformation
(e.g. filtering) and definition and application of complex
models (e.g. deformable models). Results: Volume projections
produce an impression of the 3D data set without reducing the
data amount. This supports the interpretation of the 3D data
set and saves time in comparison to any investigation which
requires examination of all slice images. More advanced
techniques for visualization, e.g. surface projections and
hybrid rendering visualize anatomical information to a very
detailed extent, but both techniques require the segmentation
of the structures of interest. Image analysis methods can be
used to extract these structures(e.g. an organ)from the image
data. Discussion:At the present time volume projections are
robust and fast enough to be used routinely. Surface
projections can be used to visualize complex and presegmented
anatomical features.
- Hagenlocker, M, and Fujimura, K, "CFFD: a tool for designing flexible shapes," VISUAL COMPUTER, vol. 14, pp. 271-287, 1998.
Abstract:
This paper describes a solid deformation method, composed free-
form deformation (CFFD), which applies a sequence of uniform
periodic B-spline FFDs over 3D Euclidean space. The
construction of the individual FFDs, which are defined by
unbounded control lattices, is described, concentrating on a
method by which feature point trajectories are employed to
control lattice point displacements. Problems due to mutual
influence of feature point trajectories are discussed, and
frozen points, which inhibit deformation in their proximity,
are introduced. Also, methods for constructing the FFD lattices
are proposed which control mutual influence of feature point
trajectories. The paper addresses computational issues of
constructing and applying CFFDs, and discusses the application
of CFFD to 3D design and animation.
- Montagnat, J, and Delingette, H, "Globally constrained deformable models for 3D object reconstruction," SIGNAL PROCESSING, vol. 71, pp. 173-186, 1998.
Abstract:
To achieve geometric reconstruction from 3D datasets two
complementary approaches have been widely used. On one hand,
the deformable model framework locally applies forces to fit
the data. On the other hand, the non-rigid registration
framework computes a global transformation minimizing the
distance between a template and the data. We first show that
applying a global transformation on a surface template, is
equivalent to applying certain global forces on a deformable
model. Second, we propose a scheme which combines the
registration and free-form deformation. This globally
constrained deformation scheme allows us to control the amount
of deformation from the reference shape with a single
parameter. Finally, we propose a general algorithm for
performing model-based reconstruction in a robust and accurate
manner. Examples on both range data and medical images are used
to illustrate and validate the globally constrained deformation
framework. (C) 1998 Elsevier Science B.V. All rights reserved.
- Jain, AK, Zhong, Y, and Dubuisson-Jolly, MP, "Deformable template models: A review," SIGNAL PROCESSING, vol. 71, pp. 109-129, 1998.
Abstract:
In this paper, we review the recently published work on
deformable models. We have chosen to concentrate on 2D
deformable models and relate the energy minimization approaches
to the Bayesian formulations. We categorize the various active
contour systems according to the definition of the deformable
model. We also present in detail one particular formulation for
deformable templates which combines edge, texture, color and
region information for the external energy and model
deformations using wavelets, splines or Fourier descriptors. We
explain how these models can be used for segmentation, image
retrieval in a large database and object tracking in a video
sequence. (C) 1998 Elsevier Science B.V. All rights reserved.
- Fujimura, K, and Makarov, M, "Foldover-free image warping," GRAPHICAL MODELS AND IMAGE PROCESSING, vol. 60, pp. 100-111, 1998.
Abstract:
An image warping method is presented that deforms an image
continuously without foldover, while observing a given set of
trajectories of feature elements. Any intermediate image during
the morph is homeomorphic to the initial image and the morphing
process is a homotopy. The method permits points, line-
segments, and polygons to be included as features in the image.
Our method is based on time-varying triangulation, that is,
triangulation changes as features move. Accordingly, the
deformation mapping is updated locally for the part for which
the triangulation changes. Experimental results are included to
demonstrate the feasibility of our approach and the complexity
of the algorithm is analyzed. (C) 1998 Academic Press.
- Ip, HHS, and Shen, DG, "An affine-invariant active contour model (AI-snake) for model- based segmentation," IMAGE AND VISION COMPUTING, vol. 16, pp. 135-146, 1998.
Abstract:
In this paper, we show that existing shaped-based active
contour models are not affine-invariant and we addressed the
problem by presenting an affine-invariant snake model (AI-
snake) such that its energy function are defined in terms local
and global affine-invariant features. The main characteristic
of the AI-snake is that, during the process of object
extraction, the pose of the model contour is dynamically
adjusted such that it is in alignment with the current snake
contour by solving the snake-prototype correspondence problem
and determining the required affine transformation. In
addition, we formulate the correspondence matching between the
snake and the object prototype as an error minimization process
between two feature vectors which capture both local and global
deformation information. We show that the technique is robust
against object deformations and complex scenes. (C) 1998
Elsevier Science B.V.
- Edwards, GJ, Lanitis, A, Taylor, CJ, and Cootes, TF, "Statistical models of face images - Improving specificity," IMAGE AND VISION COMPUTING, vol. 16, pp. 203-211, 1998.
Abstract:
Model-based approaches to the interpretation of face images
have proved very successful. We have previously described
statistically based models of face shape and grey-level
appearance and shown bow they can be used to perform various
coding and interpretation tasks. In the paper we describe
improved methods of modelling which couple shape and grey-level
information more directly than our existing methods, isolate
the changes in appearance due to different sources of
variability (person, expression, pose, lighting) and deal with
non-linear shape variation. We show that the new methods are
better suited to interpretation and tracking tasks. (C) 1998
Elsevier Science B.V.
- Glasbey, CA, and Mardia, KV, "A review of image-warping methods," JOURNAL OF APPLIED STATISTICS, vol. 25, pp. 155-171, 1998.
Abstract:
Image warping is a transformation which maps all positions in
one image plane to positions in a second plane. It arises in
many image analysis problems, whether in order to remove
optical distortions introduced by a camera or a particular
viewing perspective, to register art image with a map or
template, or to align two or more images. The choice of warp is
a compromise between a smooth distortion and one which achieves
a good match. Smoothness can be ensured by assuming a
parametric form for the warp or by constraining it using
differential equations. Matching can be specified by points to
be brought into alignment, by local measures of correlation
between images, or by the coincidence of edges. Parametric and
non-parametric approaches to warping, and matching criteria,
are reviewed.
- Garrido, A, and De la Blanca, NP, "Physically-based active shape models: Initialization and optimization," PATTERN RECOGNITION, vol. 31, pp. 1003-1017, 1998.
Abstract:
In this paper we describe a new approach for 2-D object
segmentation using an automatic method applied on images with
problems as partial information, overlapping objects, many
objects in a single scene, severe noise conditions and locating
objects with a very high degree of deformation. We use a
physically-based shape model to obtain a deformable template,
which is defined on a canonical orthogonal coordinate system.
The proposed methodology works starting from the output of an
edge detector, which is processed to automatically obtain an
approximation of the shape. The final estimation of the shapes
is obtained fitting a deformable template model, which is
defined on a learned surface of deformation. Results from
biological images are presented. (C) 1998 Pattern Recognition
Society. Published by Elsevier Science Ltd. All rights
reserved.
- Ivins, JP, and Porrill, J, "A deformable model of the human iris for measuring small three- dimensional eye movements," MACHINE VISION AND APPLICATIONS, vol. 11, pp. 42-51, 1998.
Abstract:
This paper describes a deformable model of the human iris which
forms part of a system for accurate offline measurement of
binocular three-dimensional eye movements, particularly
cyclotorsion (torsion), from video image sequences. At least
two existing systems measure torsion from infrared video images
by pupil tracking followed by cross correlation using arcs of
bandpass-filtered iris texture. Unfortunately, pupil expansion
and contraction reduces the accuracy of this method unless
drugs are used to constrict the pupil, which causes temporary
blurred vision. A five-parameter deformable model of the iris
is therefore developed for analysing images obtained without
the use of drugs. This model can translate (horizontal and
vertical eye motion), rotate (torsion) and scale both uniformly
and radially (pupil changes). Torsion measurements obtained
with the model are repeatable and accurate to within 0.1
degrees; this performance in illustrated by analysing binocular
torsion during fixation on a stationary target.
- Vetter, T, "Synthesis of novel views from a single face image," INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 28, pp. 103-116, 1998.
Abstract:
Images formed by a human face change with viewpoint. A new
technique is described for synthesizing images of faces from
new viewpoints, when only a single 2D image is available. A
novel 2D image of a face can be computed without explicitly
computing the 3D structure of the head. The technique draws on
a single generic 3D model of a human head and on prior
knowledge of faces based on example images of other faces seen
in different poses. The example images are used to "learn" a
pose-invariant shape and texture description of a new face. The
3D model is used to solve the correspondence problem between
images showing faces in different poses. The proposed method is
interesting for view independent face recognition tasks as well
as for image synthesis problems in areas like teleconferencing
and virtualized reality.
- Gu, C, and Lee, MC, "Semiautomatic segmentation and tracking of semantic video objects," IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 8, pp. 572-584, 1998.
Abstract:
This paper introduces a novel semantic video object extraction
system using mathematical morphology and a perspective motion
model. Inspired by the results from the study of the human
visual system, we intend to solve the semantic video object
extraction problem in two separate steps: supervised I-frame
segmentation, and unsupervised P-frame tracking. First, the
precise semantic video object boundary can be found using a
combination of human assistance and a morphological
segmentation tool. Second, the semantic video objects in the
remaining frames are obtained using global perspective motion
estimation and compensation of the previous semantic video
object plus boundary refinement as used for I frames.
- Lepsoy, S, and Curinga, S, "Conversion of articulatory parameters into active shape model coefficients for lip motion representation and synthesis," SIGNAL PROCESSING-IMAGE COMMUNICATION, vol. 13, pp. 209-225, 1998.
Abstract:
Speech-driven facial animation combines techniques from
different disciplines such as image analysis, computer
graphics, and speech analysis. Active shape models (ASM) used
in image analysis are excellent tools for characterizing lip
contour shapes and approximating their motion in image
sequences. By controlling the coefficients for an ASM, such a
model can also be used for animation. We design a mapping of
the articulatory parameters used in phonetics into ASM
coefficients that control nonrigid lip motion. The mapping is
designed to minimize the approximation error when articulatory
parameters measured on training lip contours are taken as input
to synthesize the training lip movements. Since articulatory
parameters can also be estimated from speech, the proposed
technique can form an important component of a speech-driven
facial animation system. (C) 1998 Elsevier Science B.V. All
rights reserved.
- Ivins, J, and Porrill, J, "Constrained active region models for fast tracking in color image sequences," COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 72, pp. 54-71, 1998.
Abstract:
Image segmentation is a fundamental problem in computer vision,
for which deformable models offer a partial solution. Most
deformable models work by performing some kind of edge
detection; complementary region growing methods have not often
been used. As a result, deformable models that track regions
rather than edges have yet to be developed to a great extent.
Active region models are a relatively new type of deformable
model driven by a region energy that is a function of the
statistical characteristics of an image. This paper describes
the use of constrained active region models for frame-rate
tracking in color video images on widely available computer
hardware. Two of the many color representations now in use are
reviewed for this purpose: the intensity-based RGB space and
the more intuitive HSV space. Normalized RGB, which is
essentially a measure of hue and saturation, emerges as the
preferred representation because it is invariant to
illumination changes and can be obtained from many frame-
grabbers via a simple fast software transformation. Three types
of motion are examined for constraining deformable models:
rigid models can only translate and rotate to fit image
features; conformal models can also change size; affine models
exhibit two kinds of shearing in addition to the other
components. Two methods are described for producing affine
motion, given the desired unconstrained motion calculated by
searching for local energy minima lying perpendicular to the
model boundary. An existing method, based on iterative gradient
descent, computes translating, rotating, scaling, and shearing
forces which can be combined to produce affine and other types
of motion. A faster, more accurate method uses least-squares
minimization to approximate the desired motion; with this
method it is also possible to derive specific equations for
rigid and conformal motion and to correct for the aperture
problem associated with the perpendicular search method. The
advantages of the new least-squares method are illustrated by
using it to drive an active region model via an affine
transformation which tracks the movements of a robot arm at
frame rate in color video images, (C) 1998 Academic Press.
- Cross, ADJ, and Hancock, ER, "Graph matching with a dual-step EM algorithm," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 20, pp. 1236-1253, 1998.
Abstract:
This paper describes a new approach to matching geometric
structure in 2D point-sets. The novel feature is to unify the
tasks of estimating transformation geometry and identifying
point-correspondence matches. Unification is realized by
constructing a mixture model over the bipartite graph
representing the correspondence match and by affecting
optimization using the EM algorithm. According to our EM
framework, the probabilities of structural correspondence gate
contributions to the expected likelihood function used to
estimate maximum likelihood transformation parameters. These
gating probabilities measure the consistency of the matched
neighborhoods in the graphs. The recovery of transformational
geometry and hard correspondence matches are interleaved and
are realized by applying coupled update operations to the
expected log-likelihood function. In this way, the two
processes bootstrap one another. This provides a means of
rejecting structural outliers. We evaluate the technique on two
real-world problems. The first involves the matching of
different perspective views of 3.5-inch floppy discs. The
second example is furnished by the matching of a digital map
against aerial images that are subject to severe barrel
distortion due to a line-scan sampling process. We complement
these experiments with a sensitivity study based on synthetic
data.
- Wang, YM, and Staib, LH, "Elastic model based non-rigid registration incorporating statistical shape information," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI'98, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1496, pp. 1162-1173, 1998.
Abstract:
This paper describes a new method of non-rigid registration
using the combined power of elastic and statistical shape
models. The transformations are constrained to be consistent
with a physical model of elasticity to maintain smoothness and
continuity. A Bayesian formulation, based on this model, on an
intensity similarity measure, and on statistical shape
information embedded in corresponding boundary points, is
employed to find a more accurate and robust nan-rigid
registration. A dense set of forces arises from the intensity
similarity measure to accommodate complex anatomical details. A
sparse set of forces constrains consistency with statistical
shape models derived from a training set. A number of
experiments were performed on both synthetic and real medical
images of the brain and heart to evaluate the approach. It is
shown that statistical boundary shape information significantly
augments and improves elastic model based non-rigid
registration.
- Fleute, M, and Lavallee, S, "Building a complete surface model from sparse data using statistical shape models: Application to computer assisted knee surgery," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI'98, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1496, pp. 879-887, 1998.
Abstract:
This paper addresses the problem of extrapolating very few
range data to obtain a complete surface representation of an
antomical structure. A new method that uses statistical shape
models is proposed and its application to modeling a few points
manually digitized on the femoral surface is detailed, in order
to improve visualization of a system developped by TIMC
laboratory for computer assisted anterior cruciate ligament
(ACL) reconstruction. The model is built from a population of
11 femur specimen digitized manually. Data sets are registered
together using an elastic registration method of Szeliski and
Lavallee based on octree-splines. Principal Components Analysis
(PCA) is performed on a field of surface deformation vectors.
Fitting this statistical model to a few points is performed by
non-linear optimisation. Results are presented for both
simulated and real data. The method is very flexible and can be
applied to any structures for which the shape is stable.
- Duta, N, and Sonka, M, "Segmentation and interpretation of MR brain images: An improved active shape model," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 17, pp. 1049-1062, 1998.
Abstract:
This paper reports a novel method for fully automated
segmentation that is based on description of shape and its
variation using point distribution models (PDM's). An
improvement of the active shape procedure introduced by Cootes
and Taylor to find new examples of previously learned shapes
using PDM's is presented, The new method for segmentation and
interpretation of deep neuroanatomic structures such as
thalamus, putamen, ventricular system, etc. incorporates a
priori knowledge about shapes of the neuroanatomic structures
to provide their robust segmentation and labeling in magnetic
resonance (MR) brain images. The method was trained in eight MR
brain images and tested in 19 brain images by comparison to
observer-defined independent standards. Neuroanatomic
structures in all testing images were successfully identified.
Computer-identified and observer-defined neuroanatomic
structures agreed well, The average labeling error was 7% +/-
3%, Border positioning errors were quite small, with the
average border positioning error of 0.8 +/- 0.1 pixels in 256 x
256 MR images, The presented method was specifically developed
for segmentation of neuroanatomic structures in MR brain
images. However, it is generally applicable to virtually any
task involving deformable shape analysis.
- Grenander, U, and Miller, MI, "Computational anatomy: An emerging discipline," QUARTERLY OF APPLIED MATHEMATICS, vol. 56, pp. 617-694, 1998.
Abstract:
This paper studies mathematical methods in the emerging new
discipline of Computational Anatomy. Herein we formalize the
Brown/Washington University model of anatomy following the
global pattern theory introduced in [1, 2], in which anatomies
are represented as deformable templates, collections of 0, 1,
2, 3-dimensional manifolds. Typical structure is carried by the
template with the variabilities accommodated via the
application of random transformations to the background
manifolds. The anatomical model is a quadruple (Omega, H, I,
P), the background space Omega = boolean ORalpha M-alpha of 0,
1, 2, 3-dimensional manifolds, the set of diffeomorphic
transformations on the background space H : Omega <-> Omega,
the space of idealized medical imagery I, and P the family of
probability measures on H. The group of diffeomorphic
transformations H is chosen to be rich enough so that a large
family of shapes may be generated with the topologies of the
template maintained. For normal anatomy one deformable template
is studied, with (Omega, H, I) corresponding to a homogeneous
space [3], in that it can be completely generated from one of
its elements, I = HItemp,I-temp is an element of I. For
disease, a family of templates boolean ORalphaItempalpha are
introduced of perhaps varying dimensional transformation
classes. The complete anatomy is a collection of homogeneous
spaces I-total = boolean ORalpha(I-alpha,H-alpha). There are
three principal components to computational anatomy studied
herein. (1) Computation of large deformation maps: Given any
two elements I, I' is an element of I in the same homogeneous
anatomy (Omega, H, I), compute diffeomorphisms h from one
anatomy to the other I (h-1)reversible arrow(h) I'. This is the
principal method by which anatomical structures are understood,
transferring the emphasis from the images I is an element of I
to the structural transformations h is an element of H that
generate them. (2) Computation of empirical probability laws:
Given populations of anatomical imagery and diffeomorphisms
between them I h(n-1)reversible arrow(hn) I-n, n = 1, . . . ,
N, generate probability laws P is an element of P on H that
represent the anatomical variation reflected by the observed
population of diffeomorphisms h(n), n = 1,..., N. (3) Inference
and disease testing: Within the anatomy (Omega, H, I, P),
perform Bayesian classification and testing for disease and
anomaly.
|
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1999 |
- Luo, B, and Hancock, ER, "Procrustes alignment with the EM algorithm," COMPUTER ANALYSIS OF IMAGES AND PATTERNS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1689, pp. 623-631, 1999.
Abstract:
This paper casts the problem of point-set alignment via
Procrustes analysis into a maximum likelihood framework using
the EM algorithm. The aim is to improve the robustness of the
Procrustes alignment to noise and clutter. By constructing a
Gaussian mixture model over the missing correspondences between
individual points, we show how alignment can be realised by
applying singular value decomposition to a weighted point
correlation matrix. Moreover, by gauging the relational
consistency of the assigned correspondence matches, we can edit
the point sets to remove clutter. We illustrate the
effectiveness of the method matching stereogram. We also
provide a sensitivity analysis to demonstrate the operational
advantages of the method.
- Chui, H, Rambo, J, Duncan, J, Schultz, R, and Rangarajan, A, "Registration of cortical anatomical structures via robust 3D point matching," INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1613, pp. 168-181, 1999.
Abstract:
Inter-subject non-rigid registration of cortical anatomical
structures as seen in MR is a challenging problem. The
variability of the sulcal and gyral patterns across patients
makes the task of registration especially difficult regardless
of whether voxel- or feature-based techniques are used. In this
paper, we present an approach to matching sulcal point features
interactively extracted by neuroanatomical experts. The robust
point matching (RPM) algorithm is used to find the optimal
affine transformations for matching sulcal points. A 3D
linearly interpolated non-rigid warping is then generated for
the original image volume. We present quantitative and visual
comparisons between Talairach, mutual information-based
volumetric matching and RPM on five subjects' MR images.
- Cootes, TF, Beeston, C, Edwards, GJ, and Taylor, CJ, "A unified framework for atlas matching using Active Appearance Models," INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1613, pp. 322-333, 1999.
Abstract:
We propose to use statistical models of shape and texture as
deformable anatomical atlases. By training on sets of labelled
examples these can represent both the mean structure and
appearance of anatomy in medical images, and the allowable
modes of deformation. Given enough training examples such a
model should be able synthesise any image of normal anatomy. By
finding the parameters which minimise the difference between
the synthesised model image and the target image we can locate
all the modelled structure. This potentially time consuming
step can be solved rapidly using the Active Appearance Model
(AAM). In this paper we describe the models and the AAM
algorithm and demonstrate the approach on structures in MR
brain cross-sections.
- Brett, AD, and Taylor, CJ, "A framework for automated landmark generation for automated 3D statistical model construction," INFORMATION PROCESSING IN MEDICAL IMAGING, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1613, pp. 376-381, 1999.
Abstract:
We describe a method of pairwise 3D surface correspondence for
the automated generation of landmarks on a set of examples from
a class of shape. We show how the pairwise corresponder can be
used in an extension of an existing framework for establishing
dense correspondences between a set of training examples to
build a 3D statistical model. The framework relies upon
additional algorithms for the production of surface paths
between vertices on a polyhedral mesh, and these are described.
An example statistical model is shown for the left lateral
ventricle of the brain.
- Velasco, HMG, Aligue, FJL, Orellana, CJG, Macias, MM, and Sotoca, MIA, "Application of ANN techniques to automated identification of bovine livestock," ENGINEERING APPLICATIONS OF BIO-INSPIRED ARTIFICIAL NEURAL NETWORKS, VOL II, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1607, pp. 422-431, 1999.
Abstract:
In this work a classification system is presented that, taking
lateral images of cattle as inputs, is able to identify the
animals and classify them by breed into previously learnt
classes. The system consists of two fundamental parts. In the
first one, a deformable-model-based preprocessing of the image
is made, in which the contour of the animal in the photograph
is sought, extracted, and normalized. Next, a neural classifier
is presented that, supplemented with a decision-maker at its
output, makes the distribution into classes. In the last part,
the results obtained in a real application of this methodology
are presented.
- Germond, L, Dojat, M, Taylor, C, and Garbay, C, "A multi-agent system for MRI brain segmentation," ARTIFICIAL INTELLIGENCE IN MEDICINE, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, vol. 1620, pp. 423-432, 1999.
Abstract:
In this paper we present an original approach for the
segmentation of MRI brain images which is based on a
cooperation between low-level and high-level approches. MRI
brain images are very difficult to segment mainly due to the
presence of inhomogeneities within tissues and also due to the
high anatomical variability of the brain topology between
individuals. In order to tackle these difficulties, we have
developped a method whose characteristics are : (i) the use of
a priori knowledge essentially anatomical and model-based; (ii)
a multi-agent system (MAS) for low-level region segmentation;
(iii) a cooperation between a priori knowledge and low-level
segmentation to guide and constrain the segmentation processes.
These characteristics allow to produce an automatic detection
of the main tissues of the brain. The method is validated with
phantoms and real images through comparisons with another
widely used approach (SPM).
- Hutton, TJ, Hammond, P, and Davenport, JC, "Active shape models for customised prosthesis design," ARTIFICIAL INTELLIGENCE IN MEDICINE, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, vol. 1620, pp. 448-452, 1999.
Abstract:
Images and computer graphics play an increasingly important
role in the design and manufacture of medical prostheses and
implants. Images provide guidance on optimal design in terms of
location, preparation and the overall shape and configuration
of subcomponents. Direct manipulation of a graphical
representation provides a natural design environment. RaPiD is
a CAD-like knowledge-based assistant for designing a dental
prosthesis known as a removable partial denture (RPD). The
expertise embedded in RaPiD encourages optimal subcomponent
configuration, but currently supports only minor customisation.
This paper describes how oral images and Active Shape Models
(ASMs) are being used to address this limitation.
- Kelemen, A, Szekely, G, and Gerig, G, "Elastic model-based segmentation of 3-D neuroradiological data sets," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 18, pp. 828-839, 1999.
Abstract:
This paper presents a new technique for the automatic model-
based segmentation of three-dimensional (3-D) objects from
volumetric image data. The development closely follows the
seminal work of Taylor and Cootes on active shape models, but
is based on a hierarchical parametric object description rather
than a point distribution model, The segmentation system
includes both the building of statistical models and the
automatic segmentation of new image data sets via a restricted
elastic deformation of shape models, Geometric models are
derived from a sample set of image data which have been
segmented by experts, The surfaces of these binary objects are
converted into parametric surface representations, which are
normalized to get an invariant object-centered coordinate
system, Surface representations are expanded into series of
spherical harmonics which provide parametric descriptions of
object shapes. It is shown that invariant object surface
parametrization provides a good approximation to automatically
determine object homology in terms of sets of corresponding
sets of surface points. Gray-level information near object
boundaries is represented by 1-D intensity profiles normal to
the surface. Considering automatic segmentation of brain
structures as our driving application, our choice of
coordinates for object alignment was the well-accepted
stereotactic coordinate system. Major variation of object
shapes around the mean shape, also referred to as shape
eigenmodes, are calculated in shape parameter space rather than
the feature space of point coordinates, Segmentation makes use
of the object shape statistics by restricting possible elastic
deformations into the range of the training shapes, The mean
shapes are initialized in a new data set by specifying the
landmarks of the stereotactic coordinate system, The model
elastically deforms, driven by the displacement forces across
the object's surface, which are generated by matching local
intensity profiles. Elastical deformations are limited by
setting bounds for the maximum variations in eigenmode space.
The technique has been applied to automatically segment left
and right hippocampus, thalamus, putamen, and globus pallidus
from volumetric magnetic resonance scans taken from
schizophrenia studies. The results have been validated by
comparison of automatic segmentation with the results obtained
by interactive expert segmentation.
- Lotjonen, J, Magnin, IE, Nenonen, J, and Katila, T, "Reconstruction of 3-D geometry using 2-D profiles and a geometric prior model," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 18, pp. 992-1002, 1999.
Abstract:
A method has been developed to reconstruct three-dimensional
(3-D) surfaces from two-dimensional (2-D) projection data. It
is used to produce individualized boundary element models,
consisting of thorax and lung surfaces, for electro- and
magnetocardiographic inverse problems. Two orthogonal
projections are utilized, A geometrical prior model, built:
using segmented magnetic resonance images, is deformed
according to profiles segmented from projection images. In our
method, virtual X-ray images of the prior model are first
constructed by simulating real X-ray imaging, The 2-D profiles
of the model are segmented from the projections and elastically
matched with the profiles segmented from patient data. The
displacement vectors produced by the elastic 2-D matching are
back projected onto the 3-D surface of the prior model.
Finally, the model is deformed, using the back-projected
vectors. Two different deformation methods are proposed, The
accuracy of the method is validated by a simulation, The
average reconstruction error of a thorax and lungs was 1.22
voxels, corresponding to about 5 mm.
- Chesnaud, C, Refregier, P, and Boulet, V, "Statistical region snake-based segmentation adapted to different physical noise models," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 21, pp. 1145-1157, 1999.
Abstract:
Algorithms for object segmentation are crucial in many image
processing applications. During past years, active contour
models (snakes) have been widely used for finding the contours
of objects. This segmentation strategy is classically edge-
based in the sense that the snake is driven to fit the maximum
of an edge map of the scene. In this paper, we propose a region
snake approach and we determine fast algorithms for the
segmentation of an object in an image. The algorithms developed
in a Maximum Likelihood approach are based on the calculation
of the statistics of the inner and the outer regions (defined
by the snake). It has thus been possible to develop optimal
algorithms adapted to the random fields which describe the gray
levels in the input image if we assume that their probability
density function family are known. We demonstrate that this
approach is still efficient when no boundary's edge exists in
the image. We also show that one can obtain fast algorithms by
transforming the summations over a region, for the calculation
of the statistics, into summations along the boundary of the
region. Finally, we will provide numerical simulation results
for different physical situations in order to illustrate the
efficiency of this approach.
- Stammberger, T, Eckstein, F, Michaelis, M, Englmeier, KH, and Reiser, M, "Interobserver reproducibility of quantitative cartilage measurements: Comparison of B-spline snakes and manual segmentation," MAGNETIC RESONANCE IMAGING, vol. 17, pp. 1033-1042, 1999.
Abstract:
The objective of this work was to develop a segmentation
technique for thickness measurements of the articular cartilage
in MR images and to assess the interobserver reproducibility of
the method in comparison with manual segmentation. The
algorithm is based on a B-spline snakes approach and is able to
delineate the cartilage boundaries in real time and with
minimal user interaction. The interobserver reproducibility of
the method, ranging from 3.3 to 13.6% for various section
orientations and joint surfaces, proved to be significantly
superior to manual segmentation. (C) 1999 Elsevier Science Inc.
- Craw, I, Costen, N, Kato, T, and Akamatsu, S, "How should we represent faces for automatic recognition?," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 21, pp. 725-736, 1999.
Abstract:
We describe results obtained from a testbed used to investigate
different codings for automatic face recognition. An eigenface
coding of shape-free faces using manually located landmarks was
more effective than the corresponding coding of correctly
shaped faces. Configuration also proved an effective method of
recognition, with rankings given to incorrect matches
relatively uncorrelated with those from shape-free faces. Both
sets of information combine to improve significantly the
performance of either system. The addition of a system, which
directly correlated the intensity values of shape-free images,
also significantly increased recognition, suggesting extra
information was still available. The recognition advantage for
shape-free faces reflected and depended upon high-quality
representation of the natural facial variation via a disjoint
ensemble of shape-free faces; if the ensemble was comprised of
nonfaces, a shape-free disadvantage was induced. Manipulation
within the shape-free coding to emphasize distinctive features
of the faces, by caricaturing, allowed further increases in
performance; this effect was only noticeable when the
independent shape-free and configuration coding was used. Taken
together, these results strongly support the suggestion that
faces should be considered as lying in a high-dimensional
manifold, which is locally linearly approximated by these
shapes and textures, possibly with a separate system for local
features. Principal Components Analysis is then seen as a
convenient tool in this local approximation.
- Egmont-Petersen, M, and Arts, T, "Recognition of radiopaque markers in X-ray images using a neural network as nonlinear filter," PATTERN RECOGNITION LETTERS, vol. 20, pp. 521-533, 1999.
Abstract:
Neural networks are developed to recognise radiopaque markers
in biplane cineangiographic video-images with a background
composed of different objects. Our connectionist approach is
compared theoretically as well as experimentally with linear
template matching. Theoretically, neural networks are likely to
give better recognition results as they can implement nonlinear
discriminants. Experiments confirm that the networks result in
better marker recognitions than template matching. (C) 1999
Elsevier Science B.V. All rights reserved.
- Cootes, TF, and Taylor, CJ, "A mixture model for representing shape variation," IMAGE AND VISION COMPUTING, vol. 17, pp. 567-573, 1999.
Abstract:
The shape variation displayed by a class of objects can be
represented as probability density function, allowing us to
determine plausible and implausible examples of the class.
Given a training set of example shapes we can align them into a
common co-ordinate frame and use kernel-based density
estimation techniques to represent this distribution. Such an
estimate is complex and expensive, so we generate a simpler
approximation using a mixture of gaussians. We show how to
calculate the distribution, and how it can be used in image
search to locate examples of the modelled object in new images.
(C) 1999 Elsevier Science B.V. All rights reserved.
- Brett, AD, Hill, A, and Taylor, CJ, "A method of 3D surface correspondence and interpolation for merging shape examples," IMAGE AND VISION COMPUTING, vol. 17, pp. 635-642, 1999.
Abstract:
A method for corresponding the triangulated mesh surface
representations of two shapes is presented. It comprises a
method of polyhedral mesh decimation and a symmetric version of
the iterative Closest Point (ICP) algorithm. The method
produces a matching pair of sparse polyhedral approximations,
one for each shape surface, using a global Euclidean measure of
similarity. A method of surface patch parameterisation is
presented which uses minimal paths constructed across the
surface of a polyhedron. We describe the use of this patch
parameterisation in the interpolation of surfaces for the
construction of a merged mean shape with a densely triangulated
surface. Results are presented for the production of a binary
tree of merged biological shapes which may be used as a basis
for the automated landmarking of a set of examples. (C) 1999
Elsevier Science B.V. All rights reserved.
- Lelieveldt, BPF, van der Geest, RJ, Rezaee, MR, Bosch, JG, and Reiber, JHC, "Anatomical model matching with fuzzy implicit surfaces for segmentation of thoracic volume scans," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 18, pp. 218-230, 1999.
Abstract:
Many segmentation methods for thoracic volume data require
manual input in the form of a seed point, initial contour,
volume of interest etc. The aim of the work presented here is
to further automate this segmentation initialization step. In
this paper an anatomical modeling and matching method is
proposed to coarsely segment thoracic volume data into
anatomically labeled regions. An anatomical model of the thorax
is constructed in two steps: 1) individual organs are modeled
with blended fuzzy implicit surfaces and 2) the single organ
models are grouped into a tree structure with a solid modeling
technique named constructive solid geometry (CSG), The
combination of CSG with fuzzy implicit surfaces allows a
hierarchical scene description by means of a boundary model,
which characterizes the scene volume as a boundary potential
function. From this boundary potential, an energy function is
defined which is minimal when the model is registered to the
tissue-air transitions in thoracic magnetic resonance imaging
(MRI) data. This allows automatic registration in three steps:
feature detection, initial positioning and energy minimization,
The model matching has been validated in phantom simulations
and on 15 clinical thoracic volume scans from different
subjects. In 13 of these sets the matching method accurately
partitioned the image volumes into a set of volumes of interest
for the heart, lungs, cardiac ventricles, and thorax outlines.
The method is applicable to segmentation of various types of
thoracic MR-images, provided that a large part of the thorax is
contained in the image volume.
- Chalmond, B, and Girard, SC, "Nonlinear modeling of scattered multivariate data and its application to shape change," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 21, pp. 422-432, 1999.
Abstract:
We are given a set of points in a space of high dimension. For
instance, this set may represent many visual appearances of an
object, a face, or a hand. We address the problem of
approximating this set by a manifold in order to have a compact
representation of the object appearance. When the scattering of
this set is approximately an ellipsoid, then the problem has a
well-known solution given by Principal Components Analysis
(PCA). However, in some situations like object displacement
learning or face learning, this linear technique may be ill-
adapted and nonlinear approximation has to be introduced. The
method we propose can be seen as a Non Linear PCA (NLPCA), the
main difficulty being that the data are not ordered. We propose
an index which favors the choice of axes preserving the closest
point neighborhoods. These axes determine an order for visiting
all the points when smoothing. Finally, a new criterion, called
"generalization error," is introduced to determine the
smoothing rate, that is, the knot number for the spline
fitting. Experimental results conclude this paper: The method
is tested on artificial data and on two data bases used in
visual learning.
- Cosio, FA, and Davies, BL, "Automated prostate recognition: a key process for clinically effective robotic prostatectomy," MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol. 37, pp. 236-243, 1999.
Abstract:
Clinical trials of PROBOT, a robotic system for prostate
surgery, have shown that robotic surgery of soft tissue can be
successful. Monitoring of the progress of the resection has
shown to be a necessary feature of an effective robotic system
for prostate surgery. It should provide the surgeon with a
reliable method of assessing the cavity during resection. An
automatic system for intraoperative monitoring of the progress
of the resection during robotic prostatectomy consists of two
subsystems: real-time intraoperative imaging of the prostate
and automatic identification of the contour of the gland on
each image. The development of a fully automatic scheme for
prostate recognition on transurethral ultrasound scans is
reported. A genetic algorithm has been developed to
automatically adjust a model of the prostate boundary until an
optimum fit to the prostate in a given image is obtained. An
analysis of ifs performance on 22 different ultrasound images
showed an average error of 6.21 mm. Use of a genetic algorithm
and a constrained prostate model have shown to be a robust way
to automatically identify the prostate in ultrasound images.
The scheme is able to produce approximate prostate boundaries,
without any human intervention, on ultrasound scans of varying
quality. In addition to soft tissue robotic surgery, the
generic algorithm technique is also applicable to a wide range
of computer assisted surgical techniques.
- Gavrila, DM, "The visual analysis of human movement: A survey," COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 73, pp. 82-98, 1999.
Abstract:
The ability to recognize humans and their activities by vision
is key for a machine to interact intelligently and effortlessly
with a human-inhabited environment. Because of many potentially
important applications, "looking at people" is currently one of
the most active application domains in computer vision. This
survey identifies a number of promising applications and
provides an overview of recent developments in this domain. The
scope of this survey is limited to work on whole-body or hand
motion; it does not include work on human faces. The emphasis
is on discussing the various methodologies; they are grouped in
2-D approaches with or without explicit shape models and 3-D
approaches. Where appropriate, systems are reviewed. We
conclude with some thoughts about future directions. (C) 1999
Academic Press.
- Behiels, G, Vandermeulen, D, Maes, F, Suetens, P, and Dewaele, P, "Active Shape Model-based segmentation of digital X-ray images," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI'99, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1679, pp. 128-137, 1999.
Abstract:
We propose an improved search procedure for Active Shape Model
(ASM) based delineation of anatomical structures in digital X-
ray images. Whereas the original ASM search method (1)
iteratively improves the current estimate of the location of
boundary points by a limited least squares adjustment of the
pose and shape parameters, our method additionally requires the
subsequent changes in shape during the search to be smooth,
which is achieved by using a minimum cost path search
algorithm. We, compare the two methods oil a database of more
than 400 manual segmentations of digital X-ray images of the
femur, humerus and calcaneus. We evaluate the accuracy and
robustness of both methods using a cross-validation procedure.
- Fleute, M, and Lavallee, S, "Nonrigid 3-D/2-D registration of images using statistical models," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI'99, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1679, pp. 138-147, 1999.
Abstract:
This paper presents a new algorithm for reconstruction of 3D
shapes using a few x-ray views and a statistical model. In many
applications of surgery such as orthopedics, it is desirable to
define a surgical planning oil 3-D images and then to execute
the plan using standard registration techniques and image-
guided surgery systems. But the cost, time and x-ray dose
associated with standard pre-operative Computed Tomography
makes it difficult to use this methodology for rather standard
interventions. Instead, we propose to use. a few x-ray images
generated from a C-Arm and to build the 3-D shape of the
patient bones or organs intra-operatively, by deforming a
statistical 3-D model to the contours segmented oil the x-ray
views. In this paper, we concentrate on the application of our
method to bone reconstruction. The algorithm starts from
segmented contours of the bone oil the x-ray images and ail
initial estimate of the pose of the 3-D model in the common
coordinate system of the set of x-ray projections. The
statistical model is made of a few principal modes that are
sufficient to represent the normal anatomy. Those modes are
built by using a generalization of the Cootes and Taylor method
to 3-D surface models, previously published in MICCAI'98 by the
authors. Fitting the model to the contours is achieved by using
a generalization of the Iterative Closest Point Algorithm to
nonrigid 3D/2D registration. For pathological shapes, the
statistical model is not valid and subsequent local refinement
is necessary. First results are presented for a 3-D statistical
model of the distal part of the femur.
- Lotjonen, J, Magnin, IE, Reinhardt, L, Nenonen, J, and Katila, T, "Automatic reconstruction of 3D geometry using projections and a geometric prior model," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, MICCAI'99, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1679, pp. 192-201, 1999.
Abstract:
A method has been developed to reconstruct 3D surfaces from two
orthogonal X-ray projections. A 3D geometrical prior model,
composed of triangulated surfaces, is deformed according to
contours segmented from projection images. The contours are
segmented by a new method based on free-form deformation.
First, virtual X-ray images of the prior model are constructed
by simulating real X-ray imaging, Thereafter, the contours
segmented from the virtual projections are elastically matched
with patient data. Next, the produced 2D vectors are back-
projected onto the surface of the prior model and the prior
model is deformed using the back-projected vectors with shape-
based interpolation. The accuracy of the method is validated by
it data set, containing 20 cases. The method is applied to
reconstruct thorax and lung surfaces. The average matching
error is about 1.2 voxels, corresponding to 5 mm.
- Suri, JS, Haralick, RM, and Sheehan, FH, "Linear vs. quadratic optimization algorithms for bias correction of left ventricle chamber boundaries in low contrast projection ventriculograms produced from xray cardiac catheterization procedure," COMPUTER ANALYSIS OF IMAGES AND PATTERNS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1689, pp. 108-117, 1999.
Abstract:
Cardiac catheterization procedure produces ventriculogram which
have very low contrast in the apical, anterior and inferior
zones of the left ventricle (LV). Pixel-based classifiers
operating on these images produce boundaries which have
systematic positional and orientation bias and have a mean
error of about 10.5 mm. Using the IV convex information,
comprising of the apex and the aortic valve plane, this pa, per
presents a comparison of the linear and quadratic optimization
algorithms to remove these biases. These algorithms axe named
after the way the coefficients are computed: the identical
coefficient and the independent coefficient. Using the polyline
metric, we show that the quadratic optimization is better than
the linear optimization. We also show that the independent
coefficient method performs better than the identical
coefficient when the training data is large. The overall mean
system error was 2.49 mm while the goal set by the cardiologist
was 2.5 mm.
|
|
2000 |
- Hill, A, Taylor, CJ, and Brett, AD, "A framework for automatic landmark identification using a new method of nonrigid correspondence," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 241-251, 2000.
Abstract:
A framework for automatic landmark indentification is presented
based on an algorithm for corresponding the boundaries of two
shapes. The auto-landmarking framework employs a binary tree of
corresponded pairs of shapes to generate landmarks
automatically on each of a set of example shapes. The landmarks
are used to train statistical shape models known as Point
Distribution Models. The correspondence algorithm locates a
matching pair of sparse polygonal approximations, one for each
of a pair of boundaries by minimizing a cost function, using a
greedy algorithm. The cost function expresses the dissimilarity
in both the shape and representation error (with respect to the
defining boundary) of the sparse polygons. Results are
presented for three classes of shape which exhibit various
types of nonrigid deformation.
- Bronkorsta, PJH, Reinders, MJT, Hendriks, EA, Grimbergen, J, Heethaar, RM, and Brankenhoff, GJ, "On-line detection of red blood cell shape using deformable templates," PATTERN RECOGNITION LETTERS, vol. 21, pp. 413-424, 2000.
Abstract:
For the purpose of automating a clinical diagnostic apparatus
to quantify the deformability of human red blood cells, we
present an automated image analysis procedure for on-line
detection of the cell shape based upon the method of parametric
deformable templates. (C) 2000 Elsevier Science B.V. All rights
reserved.
- Brett, AD, and Taylor, CJ, "A method of automated landmark generation for automated 3D PDM construction," IMAGE AND VISION COMPUTING, vol. 18, pp. 739-748, 2000.
Abstract:
A previous publication has described a method of pairwise
three-dimensional (3D) surface correspondence for the automated
generation of landmarks on a set of examples from a class of
shape (A.D. Brett, A. Hill, C.J. Taylor, A method of 3D surface
correspondence for automated landmark generation, in: 8th
British Machine Vision Conference, Essex, England, September
1997, pp 709-718). In this paper we describe a set of improved
algorithms which give more accurate and more robust results. We
show how the pairwise corresponder can be used in an extension
of an existing framework for establishing dense correspondences
between a set of training examples (A. Hill, A.D. Brett, C.J.
Taylor, Automatic landmark identification using a new method of
non-rigid correspondence, in: J. Duncan, G. Gindi, (Eds.), 15th
Conference on Information Processing in Medical Imaging,
Poulteney, VT, Springer, Berlin, 1997, pp. 483-488) to build a
3D Point Distribution Model. The framework relies upon
additional algorithms for the production of surface paths
between vertices on a polyhedral mesh, and these are described.
Example statistical models are shown for both smooth synthetic
data and the left lateral ventricle of the brain, a complex
biological shape which demonstrates considerable variation
between individuals. (C) 2000 Elsevier Science B.V. All rights
reserved.
- Bowden, R, Mitchell, TA, and Sarhadi, M, "Non-linear statistical models for the 3D reconstruction of human pose and motion from monocular image sequences," IMAGE AND VISION COMPUTING, vol. 18, pp. 729-737, 2000.
Abstract:
This paper presents a model based approach to human body
tracking in which the 2D silhouette of a moving human and the
corresponding 3D skeletal structure are encapsulated within a
non-linear point distribution model. This statistical model
allows a direct mapping to be achieved between the external
boundary of a human and the anatomical position. It is shown
how this information, along with the position of landmark
features such as the hands and head can be used to reconstruct
information about the pose and structure of the human body from
a monocular view of a scene. (C) 2000 Elsevier Science B.V. All
rights reserved.
- Egmont-Petersen, M, Schreiner, U, Tromp, SC, Lehmann, TM, Slaaf, DW, and Arts, T, "Detection of leukocytes in contact with the vessel wall from in vivo microscope recordings using a neural network," IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 47, pp. 941-951, 2000.
Abstract:
Leukocytes play an important role in the host defense as they
may travel from the blood stream into the tissue in reacting to
inflammatory stimuli. The leukocyte-vessel wall interactions
are studied in post capillary vessels by intraviral video
microscopy during in vivo animal experiments. Sequences of
video images are obtained and digitized with a frame grabber. A
method for automatic detection and characterization of
leukocytes in the video images is developed. Individual
leukocytes are detected using a neural network that is trained
with synthetic leukocyte images generated using a novel
stochastic model. This model makes it feasible to generate
images of leukocytes with different shapes and sizes under
various lighting conditions. Experiments indicate that neural
networks trained with the synthetic leukocyte images perform
better than networks trained with images of manually detected
leukocytes. The best performing neural network trained with
synthetic leukocyte images resulted in an 18% larger area under
the ROC curve than the best performing neural network trained
with manually detected leukocytes.
- Zhong, Y, Jain, AK, and Dubuisson-Jolly, MP, "Object tracking using deformable templates," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 544-549, 2000.
Abstract:
We propose a novel method for object tracking using prototype-
based deformable template models. To track an object in an
image sequence, we use a criterion which combines two terms:
the frame-to-frame deviations of the object shape and the
fidelity of the modeled shape to the Input image. The
deformable template model utilizes the prior shape information
which is extracted from the previous frames along with a
systematic shape deformation scheme to model the object shape
in a new frame. The following image information Is used in the
tracking process: 1) edge and gradient information: the object
boundary consists of pixels with large image gradient, 2)
region consistency: the same object region possesses consistent
color and texture throughout the sequence, and 3) interframe
motion: the boundary of a moving object is characterized by
large interframe motion. The tracking proceeds by optimizing an
objective function which combines both the shape deformation
and the fidelity of the modeled shape to the current image (in
terms of gradient, texture, and interframe motion). The
inherent structure in the deformable template. together with
region, motion, and image gradient cues. makes the proposed
algorithm relatively insensitive to the adverse effects of weak
image features and moderate amounts of occlusion.
- Huang, CL, Wu, MS, and Jeng, SH, "Gesture recognition using the multi-PDM method and Hidden Markov Model," IMAGE AND VISION COMPUTING, vol. 18, pp. 865-879, 2000.
Abstract:
This paper introduces a multi-Principal-Distribution-Model
(PDM) method and Hidden Markov Model (I-m IM) for gesture
recognition. To track the hand-shape, it uses the PDM model
which is built by learning patterns of variability from a
training set of correctly annotated images. However, it can
only fit the hand examples that are similar to shapes of the
corresponding training set. For gesture recognition, we need to
deal with a large variety of hand-shapes. Therefore, we divide
all the training hand shapes into a number of similar groups,
with each group trained for an individual PDM shape model.
Finally, we use the HMM to determine model transition among
these PDM shape models. From the model transition sequence, the
system can identify the continuous gestures representing one-
digit or two-digit numbers. (C) 2000 Elsevier Science B.V. All
rights reserved.
- Marques, JS, and Jorge, PM, "Visual inspection of a combustion process in a thermoelectric plant," SIGNAL PROCESSING, vol. 80, pp. 1577-1589, 2000.
Abstract:
Infrared images provide useful information to inspect the
status of combustion processes. The flame geometry and
intensity depend on the combustion status and can be used for
control and monitoring purposes. Flame segmentation is
difficult since the background intensity is sometimes higher
than the flame intensity, therefore requiring the use of
sophisticated image analysis algorithms. This paper describes
methods to analyze infrared images of industrial flames and to
characterize the flame geometry. A segmentation algorithm is
proposed to separate the flame region from the background using
an image formation model, a background model and the available
shape information. Segmentation algorithms (e.g., active
contours) usually assume solid objects with sharp boundaries.
This is not true in the case of flame images. The flame is
nonhomogeneous and it has a fuzzy boundary. To circumvent this
difficulty multiple contours are used to characterize the flame
geometry. The flame shape is then obtained by robust estimation
methods, using a model of the image formation process inside
the combustion chamber. The proposed algorithm is evaluated and
used to monitor the flame characteristics in a boiler of a
thermoelectric plant. (C) 2000 Elsevier Science B.V. All rights
reserved.
- Wang, YM, and Staib, LH, "Boundary finding with prior shape and smoothness models," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 738-743, 2000.
Abstract:
We propose a unified framework for boundary finding, where a
Bayesian formulation, based on prior knowledge and the edge
information of the input image (likelihood), is employed. The
prior knowledge in our framework is based on principal
component analysis of four different covariance matrices
corresponding to independence, smoothness, statistical shape.
and combined models, respectively. indeed, snakes. modal
analysis, Fourier descriptors, and point distribution models
can be derived from or linked to our approaches of different
prior models. When the true training set does not contain
enough variability to express the full range of deformations, a
mixed covariance matrix uses a combined prior of the smoothness
and statistical variation modes. It adapts gradually to use
more statistical modes of variation as larger data sets are
available.
- Rios, HV, Solis, AL, Aguirre, E, Guerrero, L, Pena, J, and Santamaria, A, "Facial expression recognition and modeling for virtual intelligent tutoring systems," MICAI 2000: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, vol. 1793, pp. 115-126, 2000.
Abstract:
This paper describes ongoing work for developing a new
interface for intelligent tutoring systems based on recognition
and synthesis of facial expressions. This interface senses the
emotional state of the user, or his/her degree of attention,
and communicates more naturally through face animation.
- Shen, DG, and Davatzikos, C, "An adaptive-focus deformable model using statistical and geometric information," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 906-913, 2000.
Abstract:
An active contour (snake) model is presented, with emphasis on
medical imaging applications. There are three main novelties in
the proposed model. First. an attribute vector is used to
characterize the geometric structure around each point of the
snake model: the deformable model then deforms in a way that
seeks regions with similar attribute vectors. This is in
contrast to most deformable models, which deform to nearby
edges without considering geometric structure. and it was
motivated by the need to establish point-correspondences that
have anatomical meaning. Second, an adaptive-focus statistical
model has been suggested which allows the deformation of the
active contour in each stage to be influenced primarily by the
most reliable matches. Third, a deformation mechanism that is
robust to local minima is proposed by evaluating the snake
energy function on segments of the snake at a time, instead of
individual points. Various experimental results show the
effectiveness of the proposed model.
- Lelieveldt, BPF, Sonka, M, Bolinger, L, Scholz, TD, Kayser, H, van der Geest, R, and Reiber, JHC, "Anatomical modeling with fuzzy implicit surface templates: Application to automated localization of the heart and lungs in thoracic MR volumes," COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 80, pp. 1-20, 2000.
Abstract:
In this paper, a novel model-driven segmentation approach for
thoracic MR-images is presented. The goal of this work is to
coarsely, but fully automatically localize the boundary
surfaces of the heart and lungs in thoracic MR sets. The major
organs in the thorax are described in a three-dimensional
analytical model template by combining a set of fuzzy implicit
surfaces by means of constructive solid geometry and
formulating model registration as an energy minimization. The
method has been validated on 20 thoracic MR volumes from two
centers (patients and normal subjects). On average 90% of the
contour length of the heart and lung contours was localized
with sufficient accuracy (average positional error 6 mm) to
automatically provide the initial conditions for a subsequently
applied locally accurate segmentation method. (C) 2000 Academic
Press.
- Howing, F, Dooley, LS, and Wermser, D, "Fuzzy active contour model," IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, vol. 147, pp. 323-330, 2000.
Abstract:
A new method for representing and tracking of object boundaries
is presented, which allows for the integration of uncertain a
priori knowledge into an active contour model. The novel
concept of fuzzy snakes is developed to allow for an intuitive
specification of the properties of an object's boundary. This
is achieved by introducing fuzzy energy functions and
establishing a linguistic rule base, which describes each of
the fuzzy snake's segments. Furthermore the approximate length
of each contour segment may be specified to both improve the
segmentation process and to reduce computational complexity.
Experimental results demonstrate the validity of the
theoretical properties of the fuzzy snake approach, and
examples have been included illustrating the application of the
technique to complex scenes, such as medical imaging sequences.
- Suri, JS, "Computer vision, pattern recognition and image processing in left ventricle segmentation: The last 50 years," PATTERN ANALYSIS AND APPLICATIONS, vol. 3, pp. 209-242, 2000.
Abstract:
In the last decade, computer vision, pattern recognition, image
processing and cardiac researchers have given immense attention
to cardiac image analysis and modelling. This paper surveys
state-of-the-are computer vision and pattern recognition
techniques for Left Ventricle (LV) segmentation and modelling
juring the second half of the twentieth century The paper
presents the key characteristics of successful model-based
segmentation techniques for LV modelling. This sun ey paper
concludes the following: (1) any one pattern recognition or
computer vision technique is nut sufficient for accurate 2D, 3D
or 4D modelling of LV; (2) fitting mathematical models for LV
modelling have dominated in the last 15 years; (3) knowledge
extracted from the ground truth has lead to very successful
attempts at LV modelling; (4) spatial and temporal behaviour of
LV through different imaging modalities has yielded information
which has led to accurate LV modelling; and (5) not much
attention has bern paid to LV modelling validation.
- Chella, A, Frixione, M, and Gaglio, S, "Understanding dynamic scenes," ARTIFICIAL INTELLIGENCE, vol. 123, pp. 89-132, 2000.
Abstract:
We propose a framework for the representation of visual
knowledge in a robotic agent, with special attention to the
understanding of dynamic scenes. According to our approach,
understanding involves the generation of a high level,
declarative description of the perceived world. Developing such
a description requires both bottom-up, data driven processes
that associate symbolic knowledge representation structures
with the data coming out of a vision system, and top-down
processes in which high level, symbolic information is in its
turn employed to drive and further refine the interpretation of
a scene. On the one hand, the computer vision community
approached this problem in terms of 2D/3D shape reconstruction
and of estimation of motion parameters. On the Ether, the AI
community developed rich and expressive systems for the
description of processes, events, actions and, in general, of
dynamic situations. Nevertheless, these two approaches evolved
separately and concentrated on different kinds of problems. We
propose an architecture that integrates these two traditions in
a principled way. Our assumption is that a link is missing
between the two classes of representations mentioned above. In
order to fill this gap, we adopt the notion of conceptual space
(CS-Gardenfors (2000)), a representation where information is
characterized in terms of a metric space. A CS acts as an
intermediate representation between subconceptual (i.e., not
yet conceptually categorized) information, and symbolically
organized knowledge. The concepts of process and action have
immediate characterizations in terms of structures in the
conceptual space. The architecture is illustrated with
reference to an experimental setup based on a vision system
operating in a scenario with moving and interacting people. (C)
2000 Elsevier Science B.V. All rights reserved.
- Davies, ER, "Low-level vision requirements," ELECTRONICS & COMMUNICATION ENGINEERING JOURNAL, vol. 12, pp. 197-210, 2000.
Abstract:
This paper aims to help those with some experience of vision to
obtain a more in-depth understanding of the problems of low-
level vision. As it is not possible to cover everything in a
paper of this length, a carefully chosen series of cases and
case studies is presented. Relevant principles are brought out
and a set of important ground rules is presented by way of
summary.
- Hutton, TJ, Cunningham, S, and Hammond, P, "An evaluation of active shape models for the automatic identification of cephalometric landmarks," EUROPEAN JOURNAL OF ORTHODONTICS, vol. 22, pp. 499-508, 2000.
Abstract:
This paper describes an evaluation of the application of active
shape models to cephalometric landmarking. Permissible
deformations of a template were established from a training set
of hand-annotated images and the resulting model was used to
fit to unseen images. An evaluation of this technique in
comparison to the accuracy achieved by previous methods is
presented. Sixty-three randomly selected cephalograms were
tested using a drop-one-out method. On average, 13 per cent of
16 landmarks were within 1 mm, 35 per cent within 2 mm, and 74
per cent within 5 mm. It was concluded that the current
implementation does not give sufficient accuracy for completely
automated landmarking, but could be used as a timesaving tool
to provide a first-estimate location of the landmarks. The
method is also of interest because it provides a framework for
a range of future improvements.
- Brejl, M, and Sonka, M, "Object localization and border detection criteria design in edge-based image segmentation: Automated learning from examples," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 19, pp. 973-985, 2000.
Abstract:
This paper provides methodology for fully automated model-based
image segmentation. All information necessary to perform image
segmentation is automatically derived from a training set that
is presented in a form of segmentation examples, The training
set is used to construct two models representing the objects-
shape model and border appearance model. A two-step approach to
image segmentation is reported. In the first step, an
approximate location of the object of interest is determined.
In the second step, accurate border segmentation is performed.
The shape-variant Hough transform method was developed that
provides robust object localization automatically. It finds
objects of arbitrary shape, rotation, or scaling and can handle
object variability, The border appearance model was developed
to automatically design cost functions that can be used in the
segmentation criteria of edge based segmentation methods. Our
method was tested in five different segmentation tasks that
included 489 objects to be segmented. The final segmentation
was compared to manually defined borders with good results [rms
errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5
(vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders],
Two major problems of the state-of-the-art edge based image
segmentation algorithms were addressed: strong dependency on a
close-to-target initialization, and necessity for manual
redesign of segmentation criteria whenever new segmentation
problem is encountered.
- Pantic, M, and Rothkrantz, LJM, "Automatic analysis of facial expressions: The state of the art," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 1424-1445, 2000.
Abstract:
Humans detect and interpret faces and facial expressions in a
scene with little or no effort. Still, development of an
automated system that accomplishes this task is rather
difficult. There are several related problems: detection of an
image segment as a face, extraction of the facial expression
information. and classification of the expression (e.g., in
emotion categories). A system that performs these operations
accurately and in real time would form a big step in achieving
a human-like interaction between man and machine. This paper
surveys the past work in solving these problems. The capability
of the human Visual system with respect to these problems is
discussed. too, it is meant to serve as an ultimate goal and a
guide for determining recommendations for development of an
automatic facial expression analyzer.
- Wang, YM, and Staib, LH, "Physical model-based non-rigid registration incorporating statistical shape information," MEDICAL IMAGE ANALYSIS, vol. 4, pp. 7-20, 2000.
Abstract:
This paper describes two new atlas-based methods of 2D single
modality non-rigid registration using the combined power of
physical and statistical shape models. The transformations are
constrained to be consistent with the physical properties of
deformable elastic solids in the first method and those of
viscous fluids in the second, to maintain smoothness and
continuity. A Bayesian formulation, based on each physical
model, an intensity similarity measure, and statistical shape
information embedded in corresponding boundary points, is
employed to derive more accurate and robust approaches to non-
rigid registration. A dense set of forces arises from the
intensity similarity measure to accommodate complex anatomical
details. A sparse set of forces constrains consistency with
statistical shape models derived from a training set. A number
of experiments were performed on both synthetic and real
medical images of the brain and heart to evaluate the
approaches. It is shown that statistical boundary shape
information significantly augments and improves physical model-
based non-rigid registration and the two methods we present
each have advantages under different conditions. (C) 2000
Elsevier Science B.V. All rights reserved.
- Ullman, S, and Sali, E, "Object classification using a fragment-based representation," BIOLOGICALLY MOTIVATED COMPUTER VISION, PROCEEDING, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1811, pp. 73-87, 2000.
Abstract:
The tasks of visual object recognition and classification are
natural and effortless for biological visual systems, but
exceedingly difficult to replicate in computer vision systems.
This difficulty arises from the large variability in images of
different objects within a class, and variability in viewing
conditions. In this paper we describe a fragment-based method
for object classification. In this approach objects within a
class are represented in terms of common image fragments, that
are used as building blocks for representing a large variety of
different objects that belong to a common class, such as a face
or a car. Optimal Fragments are selected from a training set of
images based on a criterion of maximizing the mutual
information of the fragments and the class they represent. For
the purpose of classification the fragments are also organized
into types, where each type is a collection of alternative
fragments, such as different hairline or eye regions for Face
classification. During classification, the algorithm detects
fragments of the different types, and then combines the
evidence for the detected fragments to reach a final decision.
The algorithm verifies the proper arrangement of the fragments
and the consistency of the viewing conditions primarily by the
conjunction of overlapping fragments. The method is different
from previous part-based methods in using class-specific
overlapping object fragments of varying complexity. and in
verifying the consistent arrangement of the fragments primarily
by the conjunction of overlapping detected fragments.
Experimental results on the detection of face and car views
show that the fragment-based approach can generalize well to
completely novel image views within a class while maintaining
low mis-classification error rates. We briefly discuss
relationships between the proposed method and properties of
parts of the primate visual system involved in object
perception.
- Wu, RY, Ling, KV, and Ng, WS, "Automatic prostate boundary recognition in sonographic images using feature model and genetic algorithm," JOURNAL OF ULTRASOUND IN MEDICINE, vol. 19, pp. 771-782, 2000.
Abstract:
This paper describes the development of a model based boundary
recognition system for transrectal prostate ultrasonographic
images. It consists of two techniques: boundary modeling and
boundary searching with model constraints. To achieve higher
specificity of the model, a method called feature modeling is
derived from the existing point distribution modeling method.
To improve the robustness of the searching technique, the
genetic algorithm is used. Incremental genetic algorithm with
crowding replacement and binary string chromosome type was
found experimentally to give good search results. It was shown
that the system could recognize the boundary with considerable
accuracy and consistency within a few minutes in transrectal
ultrasonographic images taken from approximate middle position
of the prostate.
- Marais, P, and Brady, JM, "Detecting the brain surface in sparse MRI using boundary models," MEDICAL IMAGE ANALYSIS, vol. 4, pp. 283-302, 2000.
Abstract:
We introduce a framework for the detection of the brain
boundary (arachnoid) within sparse MRI. We use the term sparse
to describe volumetric images in which the sampling resolution
within the imaging plane is far higher than that of the
perpendicular direction. Generic boundary detection schemes do
not provide good results for such data. In the scheme we
propose, the boundary is extracted using a constrained mesh
surface which iteratively approximates a 3D point set
consisting of detected boundary points. Boundary detection is
based on a database of piecewise constant models, which
represent the idealised MR intensity profile of the underlying
boundary anatomy. A non-linear matching scheme is introduced to
estimate the location of the boundary points using only the
intensity data within each image plane. Results are shown for a
number of images and are discussed in detail. (C) 2000 Elsevier
Science B.V. All rights reserved.
- Lohmann, G, and von Cramon, DY, "Automatic labelling of the human cortical surface using sulcal basins," MEDICAL IMAGE ANALYSIS, vol. 4, pp. 179-188, 2000.
Abstract:
Human brain mapping aims at establishing correspondences
between brain function and brain anatomy. One of the most
intriguing problems in this field is the high interpersonal
variability of human neuroanatomy which makes studies across
many subjects very difficult. The cortical folds ('sulci')
often serve as landmarks that help to establish correspondences
between subjects. in this paper, we will present a method that
automatically detects and attributes neuroanatomical names to
the cortical folds using image analysis methods applied to
magnetic resonance data of human brains. We claim that the
cortical folds can be subdivided into a number of substructures
which we call sulcal basins. The concept of sulcal basins
allows us to establish a complete parcellation of the cortical
surface into separate regions. These regions are
neuroanatomically meaningful and can be identified from MR data
sets across many subjects. Sulcal basins are segmented using a
region growing approach. The automatic labelling is achieved by
a model matching technique. (C) 2000 Elsevier Science B.V. All
rights reserved.
- Kim, JS, Koh, KC, and Cho, HS, "An active contour model with shape regulation scheme," ADVANCED ROBOTICS, vol. 14, pp. 495-514, 2000.
Abstract:
This paper presents an active method for locating target
objects in images, which is aimed at improving the performance
of detecting object boundaries by enhancing the behavioral
characteristics of an active contour. The proposed active
contour model simulates a mechanical system consisting of two
main parts: the first is a rigid fixture, called the 'core' ,
specifying the expected shape of target boundaries, while the
second is an elastic rod attached to the rigid fixture. The
elastic rod deforms or moves relative to the rigid core
according to the classical laws of the mechanical system, When
the initial contour is applied to an image data, it is
attracted near the dominant image features, but tries to keep
its home shape and simultaneously make the deformation smooth
if a deformation is more natural for force equilibrium. This
mechanism significantly improves the performance of detecting
object boundaries in the presence of some disturbing image
features. The active contour is scale invariant, thereby
significantly relieving the difficulty in selecting proper
values for the model parameters. The values for the model
parameters can be selected to make the contour have the desired
behaviors around the equilibrium position through the analysis
of the vibration mode of the mechanical system. The performance
of the proposed method is validated through a series of
experiments, which include detection of heavily degraded
objects, tracking of objects under non-rigid motion and
comparisons with the original snake models.
- Luo, B, and Hancock, ER, "Alignment and correspondence using singular value decomposition," ADVANCES IN PATTERN RECOGNITION, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1876, pp. 226-235, 2000.
Abstract:
This paper casts the problem of point-set alignment and
correspondence into a unified framework. The utility measure
underpinning the work is the cross-entropy between probability
distributions for alignment and assignment errors. We show how
Procrustes alignment parameters and correspondence
probabilities can be located using dual singular value
decompositions. Experimental results using both synthetic and
real images are given.
- Bishop, CM, and Winn, JM, "Non-linear Bayesian image modelling," COMPUTER VISION - ECCV 2000, PT I, PROCEEDINGS, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1842, pp. 3-17, 2000.
Abstract:
In recent years several techniques have been proposed for
modelling the low-dimensional manifolds, or 'subspaces', of
natural images. Examples include principal component analysis
(as used for instance in 'eigen-faces'), independent component
analysis, and auto-encoder neural networks. Such methods suffer
from a number of restrictions such as the limitation to linear
manifolds or the absence of a probablistic representation. In
this paper we exploit recent developments in the fields of
variational inference and latent variable models to develop a
novel and tractable probabilistic approach to modelling
manifolds which can handle complex non-linearities. Our
framework comprises a mixture of sub-space components in which
both the number of components and the effective dimensionality
of the sub-spaces are determined automatically as part of the
Bayesian inference procedure. We illustrate our approach using
two classical problems: modelling the manifold of face images
and modelling the manifolds of hand-written digits.
- Rogers, M, Graham, J, and Malik, RA, "Exploiting weak shape constraints to segment capillary images in microangiopathy," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2000, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1935, pp. 717-726, 2000.
Abstract:
Microangiopathy is one form of pathology associated with
peripheral neuropathy in diabetes. Capillaries imaged by
electron microscopy show a complex textured appearance, which
makes segmentation difficult. Considerable variation occurs
among boundaries manually positioned by human experts.
Detection of region boundaries using Active Contour Models has
proved impractical due to the existence of confusing image
evidence in the vicinity of these boundaries. Despite the fact
that the shapes have no identifying landmarks, the weak
constraints imposed by statistical shape modelling combined
with genetic search can provide accurate segmentations.
- Shen, DG, and Davatzikos, C, "Adaptive-focus statistical shape model for segmentation of 3D MR structures," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2000, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1935, pp. 206-215, 2000.
Abstract:
This paper presents a deformable model for automatically
segmenting objects from volumetric MR images and obtaining
point correspondences, using geometric and statistical
information in a hierarchical scheme. Geometric information is
embedded into the model via an affine-invariant attribute
vector, which characterizes the geometric structure around each
model point from a local to a global level. Accordingly, the
model deforms seeking boundary points with similar attribute
vectors. This is in contrast to most deformable surface models,
which adapt to nearby edges without considering the geometric
structure. The proposed model is adaptive in that it initially
focuses on the most reliable structures of interest, and
subsequently switches focus to other structures as those become
closer to their respective targets and therefore more reliable.
The proposed techniques have been used to segment boundaries of
the ventricles, the caudate nucleus, and the lenticular nucleus
from volumetric MR images.
- Montagnat, J, and Delingette, H, "Space and time shape constrained deformable surfaces for 4D medical image segmentation," MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI 2000, LECTURE NOTES IN COMPUTER SCIENCE, vol. 1935, pp. 196-205, 2000.
Abstract:
The aim of this work is to automatically extract quantitative
parameters from time sequences of 3D images (4D images) suited
to heart pathology diagnosis. In this paper, we propose a
framework for the reconstruction of the left ventricle motion
from 4D images based on 4D deformable surface models. These 4D
models are represented as a time sequence of 3D meshes whose
deformation are correlated during the cardiac cycle. Both
temporal and spatial constraints based on prior knowledge of
heart shape and motion are combined to improve the segmentation
accuracy. In contrast to many earlier approaches, our framework
includes the notion of trajectory constraint. We have
demonstrated the ability of this segmentation tool to deal with
noisy or low contrast images on 4D MR, SPECT, and US images.
- Duncan, JS, and Ayache, N, "Medical image analysis: Progress over two decades and the challenges ahead," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 85-106, 2000.
Abstract:
The analysis of medical images has been woven into the fabric
of the Pattern Analysis and Machine Intelligence (PAMI)
community since the earliest days of these Transactions.
Initially, the efforts in this area were seen as applying
pattern analysis and computer vision techniques to another
interesting dataset. However, over the last two to three
decades, the unique nature of the problems presented within
this area of study have led to the development of a new
discipline in its own right. Examples of these include: the
types of image information that are acquired, the fully three-
dimensional image data, the nonrigid nature of object motion
and deformation, and the statistical variation of both the
underlying normal and abnormal ground truth. In this paper, we
look at progress in the field over the last 20 years and
suggest some of the challenges that remain for the years to
come.
- Garrido, A, and de la Blanca, NP, "Applying deformable templates for cell image segmentation," PATTERN RECOGNITION, vol. 33, pp. 821-832, 2000.
Abstract:
This paper presents an automatic method. based on the
deformable template approach, for cell image segmentation under
severe noise conditions. We define a new methodology, dividing
the process into three parts: (1) obtain evidence from the
image about the location of the cells, (2) use this evidence to
calculate an elliptical approximation of these locations; (3)
refine cell boundaries using locally deforming models. We have
designed a new algorithm to locate cells and propose an energy
function to be used together with 3 stochastic deformable
template model. Experimental results show that this approach
for segmenting cell images is both Fast and robust, and that
this methodology may be used for automatic classification as
part of a computer-aided medical decision making technique. (C)
2000 Pattern Recognition Society. Published by Elsevier Science
Ltd, All rights reserved.
- Suri, JS, Haralick, RM, and Sheehan, FH, "Greedy algorithm for error correction in automatically produced boundaries from low contrast ventriculograms," PATTERN ANALYSIS AND APPLICATIONS, vol. 3, pp. 39-60, 2000.
Abstract:
Non-homogeneous mixing of the dye with the blood in the left
ventricle chamber of the heart causes poor contrast in the
ventriculograms. The pixel-based classifiers [1] operating on
these ventriculograms yield boundaries which are not close to
ground truth boundaries as delineated by the cardiologist. They
have a mean boundary error of 6.4 mm and an error of 12.5 mm in
the apex zone. These errors have a systematic positional and
orientational bias, the boundary being under-estimated in the
apex zone. This paper discusses two calibration methods: the
identical coefficient and the independent coefficient to remove
these systematic biases. From these methods, we constitute a
fused algorithm which reduces the boundary error compared to
either of the calibration methods. The algorithm, in a greedy
way, computes which and how many vertices of the left ventricle
boundary can be taken from the computed boundary of each method
in order to best improve the performance. The corrected
boundaries have a mean error of less than 3.5 mm with a
standard deviation of 3.4 mm over the approximately 6 x 10(4)
vertices in the data set of 291 studies. Our method reduces the
mean boundary error by 2.9 mm over the boundary produced by the
classifier. We also show that the calibration algorithm
performs better in the apex zone where the dye is unable to
propagate. For end diastole, the: algorithm reduces the error
in the apex zone by 8.5 mm over the pixel-based classifier
boundaries.
- Pentland, A, "Looking at people: Sensing for ubiquitous and wearable computing," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 22, pp. 107-119, 2000.
Abstract:
The research topic of looking at people, that is, giving
machines the ability to detect, track, and identify people and
more generally, to interpret human behavior, has become a
central topic in machine vision research. Initially thought to
be the research problem that would be hardest to solve, it has
proven remarkably tractable and has even spawned several
thriving commercial enterprises. The principle driving
application for this technology is "fourth generation" embedded
computing: "smart"' environments and portable or wearable
devices. The key technical goals are to determine the
computer's context with respect to nearby humans (e.g., who,
what, when, where, and why) so that the computer can act or
respond appropriately without detailed instructions. This paper
will examine the mathematical tools that have proven
successful, provide a taxonomy of the problem domain, and then
examine the state-of-the-art. Four areas will receive
particular attention: person identification,
surveillance/monitoring, 3D methods, and smart rooms/perceptual
user interfaces. Finally, the paper will discuss some of the
research challenges and opportunities.
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2001 |
- Sui, L, Haralick, RM, and Sheehan, FH, "A knowledge-based boundary delineation system for contrast ventriculograms," IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, vol. 5, pp. 116-132, 2001.
Abstract:
Automated left-ventricle (LV) boundary delineation from
contrast ventriculograms has been studied for decades.
Unfortunately, no accurate methods have ever been reported. A
new knowledge based multistage method to automatically
delineate the LV boundary at end diastole (ED) and end systole
(ES) is discussed in this paper. It has a mean absolute
boundary error or about 2 mm and an associated ejection
fraction error of about 6%. The method makes extensive use of
knowledge about LV shape and movement, The processing includes
a multiimage pixel region classification, shape regression, and
rejection classification. The method was trained and cross-
validated tested on a database of 375 studies whose ED and ES
boundary had been manually traced as the ground truth. The
cross-validated results presented in this paper show that the
accuracy is close to and slightly above the interobserver
variability.
- Hermann, NV, Jensen, BL, Dahl, E, Darvann, TA, and Kreiborg, S, "A method for three-projection infant cephalometry," CLEFT PALATE-CRANIOFACIAL JOURNAL, vol. 38, pp. 299-316, 2001.
Abstract:
Objective: To assess morphology and growth in infants and
children with craniofacial anomalies based on comprehensive
digitization of radiographic films in three, mutually
orthogonal projections. Method: The method consists of (1)
acquisition of radiographic films in a highly standardized
three-projection (lateral, frontal, and axial) cephalometer,
(2) marking and digitization of a total of 279 anatomical
landmarks in the three projections, and (3) computation and
presentation (tabular and graphical) of 356 linear and angular
variables describing the craniofacial morphology, including
soft tissue. Computation of statistical entities describing a
patient, a group of patients, the differences between patients
or groups of patients was carried out, Error assessment of the
method involved investigation of error distribution among a
number of error sources. Duplicate digitization of radiographic
films from 30 randomly selected patients, and from in dry
skulls, was carried out to determine the errors contributed by
the procedure of landmark digitization and the distribution of
error among landmarks and variables, as well as between
projections. Results: The average error due to landmark
digitization, s(i), determined by duplicate digitization and
calculated by use of Dahlberg's formula was 0.8 mm for linear
variables and 1.6 degrees for angular variables. Conclusion:
This method of infant cephalometry has been shown to be highly
accurate and reproducible, and it adds significant new
potential for, e.g., asymmetry detection, population
comparison, and growth measurements compared to other
cephalometric techniques due to its standardized acquisition
and digitization protocol, inclusion of an axial projection,
and the large number of well-defined landmarks and variables
involved.
- Varekamp, C, and Hoekman, DH, "Segmentation of high-resolution InSAR data of a tropical forest using Fourier parameterized deformable models," INTERNATIONAL JOURNAL OF REMOTE SENSING, vol. 22, pp. 2339-2350, 2001.
Abstract:
Currently, tree maps are produced from field measurements that
are time consuming and expensive. Application of existing
techniques based on aerial photography is often hindered by
cloud cover. This has initiated research into the segmentation
of high resolution airborne interferometric Synthetic Aperture
Radar (SAR) data for deriving tree maps. A robust algorithm is
constructed to optimally position closed boundaries. The
boundary of a tree crown will be best approximated when at all
points on the boundary, the z-coordinate image gradient is
maximum, and directed inwards orthogonal to the boundary. This
property can be expressed as the result of a line integral
along the boundary. Boundaries with a large value for the line
integral are likely to be tree crowns. This paper focuses on
the search procedure and on illustrating how smoothing can be
used to prevent the search from becoming trapped in a local
optimum. The final crown detection stage is not described in
this paper but could be based on the gradient and implemented
using the above described value for the line integral. Results
of this paper indicate that a Fourier parametrization with only
three harmonics (nine parameters) can describe the shape
variation in the 2D crown projection in sufficient detail.
Current ground datasets are not suitable for obtaining
detection statistics such as the percentage of tree crowns
detected and the number of false alarms. Better ground datasets
will be needed to evaluate algorithm performance for real tree
mapping situations.
- Chang, IC, and Huang, CL, "Skeleton-based walking motion analysis using hidden Markov model and active shape models," JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, vol. 17, pp. 371-403, 2001.
Abstract:
This paper proposes a skeleton-based human walking motion
analysis system which consists of three major phases. In the
first phase, it extracts the human body skeleton from the
background and then obtains the body signatures. In the second
phase, it analyzes the training sequences to generate
statistical models. In the third phase, it uses the trained
models to recognize the input human motion sequence and
calculate the motion parameters. The experimental results
demonstrate how our system can recognize the motion type and
describe the motion characteristics of the image sequence.
Finally, the synthesized motion sequences are illustrated. The
major contributions of this paper are: (1) development of a
skeleton-based method and use of Hidden Markov Models (HMM) to
recognize the motion type; (2) incorporation of the Active
Shape Models (ASMs) and the body structure characteristics to
generate the motion parameter curves of the human motion.
- Wohler, C, and Anlauf, JK, "Real-time object recognition on image sequences with the adaptable time delay neural network algorithm - applications for autonomous vehicles," IMAGE AND VISION COMPUTING, vol. 19, pp. 593-618, 2001.
Abstract:
Within the framework of the vision-based "Intelligent Stop&Go"
driver assistance system for both the motorway and the inner
city environment, we present a system for segmentation-free
detection of overtaking vehicles and estimation of ego-position
on motorways as well as a system for the recognition of
pedestrians in the inner city traffic scenario. Both systems
are running in real-time in the test vehicle UTA of the
DaimlerChrysler computer vision lab, relying on the adaptable
time delay neural network (ATDNN) algorithm. For object
recognition, this neural network processes complete image
sequences at a time instead of single images, as it is the case
in most conventional neural algorithms. The results are
promising in that using the ATDNN algorithm, we are able to
perform the described recognition tasks in a large variety of
real-world scenarios in a computationally highly efficient and
rather robust and reliable manner. (C) 2001 Elsevier Science
Ltd All rights reserved.
- Pycock, D, Pammu, S, Goode, AJ, and Harman, SA, "Robust model-based signal analysis and identification," PATTERN RECOGNITION, vol. 34, pp. 2181-2199, 2001.
Abstract:
We describe and evaluate a model-based scheme for feature
extraction and model-based signal identification which uses
likelihood criteria for "edge" detection. Likelihood measures
from the feature identification process are shown to provide a
well behaved measure of signal interpretation confidence. We
demonstrate that complex, transient signals, from one of 6
classes, can reliably be identified at signal to noise ratios
of 2 and that identification does not fail until the signal to
noise ratio has reached 1. Results show that the loss in
identification performance resulting from the use of a
heuristic, rather than an exhaustive, search strategy is
minimal. Crown Copyright (C) 2001 Published by Elsevier Science
Ltd on behalf of the Pattern Recognition Society. All rights
reserved.
- Dubuisson-Jolly, MP, and Gupta, A, "Tracking deformable templates using a shortest path algorithm," COMPUTER VISION AND IMAGE UNDERSTANDING, vol. 81, pp. 26-45, 2001.
Abstract:
This paper proposes a new technique to track deformable
templates. We extend the typical graph algorithms that have
been used for active contour recovery to incorporate shape
information. The advantage of graph algorithms is that they are
guaranteed to find the global minimum of the energy function.
The difficulty with their traditional use for active contours
is that they consider only two pixels at a time when recovering
the contour, making it impossible to enforce shape constraints.
We define the deformable template as a polygonal contour,
demonstrate the proper mapping between the image, the contour,
and a graph, and show how to apply Dijkstra's algorithm to
track contours in image sequences. Examples are shown for
deforming contours, articulated objects, and smooth contours
being tracked in simple and complicated backgrounds. We also
provide an analysis of the computational requirements. (C) 2001
Academic Press.
- Huang, CL, and Jeng, SH, "A model-based hand gesture recognition system," MACHINE VISION AND APPLICATIONS, vol. 12, pp. 243-258, 2001.
Abstract:
This paper introduces a model-based hand gesture recognition
system, which consists of three phases: feature extraction,
training, and recognition. In the feature extraction phase, a
hybrid technique combines the spatial (edge) and the temporal
(motion) information of each frame to extract the feature
images. Then, in the training phase, we use the principal
component analysis (PCA) to characterize spatial shape
variations and the hidden Markov models (HMM) to describe the
temporal shape variations. A modified Hausdorff distance
measurement is also applied to measure the similarity between
the feature images and the pre-stored PCA models. The
similarity measures are referred to as the possible
observations for each frame. Finally, in recognition phase,
with the pre-trained PCA models and HMM, we can generate the
observation patterns from the input sequences and then apply
the Viterbi algorithm to identify the gesture. In the
experiments, we prove that our method can recognize 18
different continuous gestures effectively.
- Shearer, K, Wong, KD, and Venkatesh, S, "Combining multiple tracking algorithms for improved general performance," PATTERN RECOGNITION, vol. 34, pp. 1257-1269, 2001.
Abstract:
Automated tracking of objects through a sequence of images has
remained one of the difficult problems in computer vision.
Numerous algorithms and techniques have been proposed for this
task. Some algorithms perform well in restricted environments,
such as tracking using stationary camel as, but a general
solution is not currently available. A frequent problem is that
when an algorithm is refined for one application, it becomes
unsuitable for other applications, This paper proposes a
general tracking system based on a different approach. Rather
than refine one algorithm for a specific tracking task, two
tracking algorithms are employed, and used to correct each
other during the tracking task. By choosing the two algorithms
such that they have complementary failure modes, a robust
algorithm is created without increased specialisation. (C) 2001
Pattern Recognition Society. Published by Elsevier Science Ltd.
All rights reserved.
- Shen, DG, Herskovits, EH, and Davatzikos, C, "An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 20, pp. 257-270, 2001.
Abstract:
This paper presents a deformable model for automatically
segmenting brain structures from volumetric magnetic resonance
(MR) images and obtaining point correspondences, using
geometric and statistical information in a hierarchical scheme.
Geometric information is embedded into the model via a set of
affine-invariant attribute vectors, each of which characterizes
the geometric structure around a point of the model from a
local to a global scale, The attribute vectors, in conjunction
with the deformation mechanism of the model, warranty that the
model not only deforms to nearby edges, as is customary in most
deformable surface models, but also that it determines point
correspondences based on geometric similarity at different
scales. The proposed model is adaptive in that it initially
focuses on the most reliable structures of interest, and
gradually shifts focus to other structures as those become
closer to their respective targets and, therefore, more
reliable. The proposed techniques have been used to segment
boundaries of the ventricles, the caudate nucleus, and the
lenticular nucleus from volumetric MR images.
- Chang, CC, and Tsai, WH, "Vision-based tracking and interpretation of human leg movement for virtual reality applications," IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, vol. 11, pp. 9-24, 2001.
Abstract:
A vision-based system for tracking and interpreting leg motion
in image sequences using a single camera is developed for a
user to control his movement in the virtual world by his legs.
Twelve control commands are defined. The trajectories of the
color marks placed on the shoes of the user are used to
determine the types of leg movement by a first-order Markov
process. Then, the types of leg movement are encoded
symbolically as input to Mealy machines to recognize the
control command associated with a sequence of leg movements.
The proposed system is implemented on a commercial PC without
any special hardware. Because the transition functions of Mealy
machines are deterministic, the implementation of the proposed
system is simple and the response time of the system is short,
Experimental results with a 14-Hz frame rate on 320 x 240 image
resolution are included to prove the feasibility of the
proposed approach.
- Sullivan, J, Blake, A, Isard, M, and MacCormick, J, "Bayesian object localisation in images," INTERNATIONAL JOURNAL OF COMPUTER VISION, vol. 44, pp. 111-135, 2001.
Abstract:
A Bayesian approach to intensity-based object localisation is
presented that employs a teamed probabilistic model of image
filter-bank output, applied via Monte Carlo methods, to escape
the inefficiency of exhaustive search. An adequate
probabilistic account of image data requires intensities both
in the foreground (i.e, over the object), and in the
background, to be modelled. Some previous approaches to object
localisation by Monte Carlo methods have used models which, we
claim, do not fully address the issue of the statistical
independence of image intensities. It is addressed here by
applying to each image a bank of filters whose outputs are
approximately statistically independent. Distributions of the
responses of individual filters, over foreground and
background, are learned from training data. These distributions
are then used to define a joint distribution for the output of
the filter bank, conditioned on object configuration, and this
serves as an observation likelihood for use in probabilistic
inference about localisation. The effectiveness of
probabilistic object localisation in image clutter, using
Bayesian Localisation, is illustrated. Because it is a Monte
Carlo method, it produces not simply a single estimate of
object configuration, but an entire sample from the posterior
distribution for the configuration. This makes sequential
inference of configuration possible. Two examples are
illustrated here: coarse to fine scale inference, and
propagation of configuration estimates over time, in image
sequences.
- Rolfe, BF, Cardew-Hall, M, Abdallah, SM, and West, GAW, "Geometric shape errors in forging: developing a metric and an inverse model," PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B- JOURNAL OF ENGINEERING MANUFACTURE, vol. 215, pp. 1229-1240, 2001.
Abstract:
The complexity of the forging process ensures that there is
inherent variability in the geometric shape of a forged part.
While knowledge of shape error, comparing the desired versus
the measured shape, is significant in measuring partiquality
the question of more interest is what can this error suggest
about the forging process set-up? The first contribution of
this paper is to develop a shape error metric which identifies
geometric shape differences that occur from a desired forged
part. This metric is based on the point distribution deformable
model developed in pattern recognition research. The second
contribution of this paper is to propose an inverse model that
identifies changes in process set-up parameter values: by
analysing the proposed shape error metric. The metric and
inverse models are developed using two sets of simulated hot-
forged parts created using two different die pairs (simple and
'M'-shaped die pairs). A neural network is used to classify the
shape data into three arbitrarily chosen levels for each
parameter and it is accurate to at least 77 per cent in the
worst case for the simple die pair data and has an average
accuracy of approximately 80 per cent when classifying the more
complex 'M'-shaped die pair data.
- Gavrila, DM, "Sensor-based pedestrian protection," IEEE INTELLIGENT SYSTEMS, vol. 16, pp. 77-81, 2001.
Abstract:
Objectives: To evaluate the potential for machine, learning
techniques to identify objective criteria for classifying
vertical facial deformity. Methods: 19 parameters were
determined from 131 lateral skull radiographs. Classifications
were induced from raw data with simple visualisation, C5.0 and
Kohonen feature maps; and using a Point Distribution Model
(PDM) of shape templates comprising points taken from digitised
radiographs. Results: The induced decision trees enable a
direct of clinicians' idiosyncrosies in classification.
Unsupervised algorithms induce models that are potentially more
objective, but their blackbox nature makes them unsuitable for
clinical application. The PDM methodology gives dramatic
visualisations of two modes separating horizontal and vertical
facial Kohonen feature maps favour one clinician and PDM the
other. Clinical response suggests that while Clinician 1 places
greater weight on 5 of 6 parameters, Clinician 2 relies on more
parameters;that capture facial shape, Conclusions: While
machine learning and statistical analyses classify subjects for
vertical facial height they have limited application in their
present form. The supervised learning algorithm C5.0 is
effective for generating rules for individual clinicians but
its inherent bias invalidates its use for objective
classification of facial form for research purposes. On the
otherhand, promising results from unsupervised strategies
(especially the POW suggest a potential use for objective
classification and further identification and analysis of
ambiguous cases. At present, such methodologies may be
unsuitable tor clinical application because of the invisibility
of their underlying processes. Further study is required with
additional patient data and a wider group of clinicians.
- Hammond, P, Hutton, TJ, Nelson-Moon, ZL, Hunt, NP, and Madgwick, AJA, "Classifying vertical facial deformity using supervised and unsupervised learning," METHODS OF INFORMATION IN MEDICINE, vol. 40, pp. 365-372, 2001.
Abstract:
Objectives: To evaluate the potential for machine, learning
techniques to identify objective criteria for classifying
vertical facial deformity. Methods: 19 parameters were
determined from 131 lateral skull radiographs. Classifications
were induced from raw data with simple visualisation, C5.0 and
Kohonen feature maps; and using a Point Distribution Model
(PDM) of shape templates comprising points taken from digitised
radiographs. Results: The induced decision trees enable a
direct of clinicians' idiosyncrosies in classification.
Unsupervised algorithms induce models that are potentially more
objective, but their blackbox nature makes them unsuitable for
clinical application. The PDM methodology gives dramatic
visualisations of two modes separating horizontal and vertical
facial Kohonen feature maps favour one clinician and PDM the
other. Clinical response suggests that while Clinician 1 places
greater weight on 5 of 6 parameters, Clinician 2 relies on more
parameters;that capture facial shape, Conclusions: While
machine learning and statistical analyses classify subjects for
vertical facial height they have limited application in their
present form. The supervised learning algorithm C5.0 is
effective for generating rules for individual clinicians but
its inherent bias invalidates its use for objective
classification of facial form for research purposes. On the
otherhand, promising results from unsupervised strategies
(especially the POW suggest a potential use for objective
classification and further identification and analysis of
ambiguous cases. At present, such methodologies may be
unsuitable tor clinical application because of the invisibility
of their underlying processes. Further study is required with
additional patient data and a wider group of clinicians.
- Montagnat, J, Delingette, H, and Ayache, N, "A review of deformable surfaces: topology, geometry and deformation," IMAGE AND VISION COMPUTING, vol. 19, pp. 1023-1040, 2001.
Abstract:
Deformable models have raised much interest and found various
applications in the fields of computer vision and medical
imaging. They provide an extensible framework to reconstruct
shapes. Deformable surfaces, in particular, are used to
represent 3D objects. They have been used for pattern
recognition [Computer Vision and Image Understanding 69(2)
(1998) 201; IEEE Transactions on Pattern Analysis and Machine
Intelligence 19(10) (1997) 1115], computer animation [ACM
Computer Graphics (SIGGRAPH'87) 21(4) (1987) 205], geometric
modelling [Computer Aided Design (CAD) 24(4) (1992) 178],
simulation [Visual Computer 16(8) (2000) 437], boundary
tracking [ACM Computer Graphics (SIGGRAPH'94) (1994) 185],
image segmentation [Computer Integrated Surgery, Technology and
Clinical Applications (1996) 59; IEEE Transactions on Medical
Imaging 14 (1995) 442; Joint Conference on Computer Vision,
Virtual Reality and Robotics in Medicine (CVRMed-MRCAS'97) 1205
(1997) 13; Medical Image Computing and Computer-Assisted
Intervention (MICCAI'99) 1679 (1999) 176; Medical Image
Analysis 1(1) (1996) 19], etc. In this paper we propose a
survey on deformable surfaces. Many surface representations
have been proposed to meet different 3D reconstruction problem
requirements. We classify the main representations proposed in
the literature and we study the influence of the representation
on the model evolution behavior, revealing some similarities
between different approaches. (C) 2001 Elsevier Science B.V.
All rights reserved.
- Inoue, A, Drummond, T, and Cipolla, R, "Real time feature-based facial tracking using Lie algebras," IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, vol. E84D, pp. 1733-1738, 2001.
Abstract:
We have developed a novel human facial tracking system that
operates in real time at a video frame rate without needing any
special hardware. The approach is based on the use of Lie
algebra, and uses three-dimensional feature points on the
targeted human face. It is assumed that the roughly estimated
facial model (relative coordinates of the three-dimensional
feature points) is known. First, the initial feature positions
of the face are determined using a model fitting technique.
Then, the tracking is operated by the following sequence: (1)
capture the new video frame and render feature points to the
image plane; (2) search for new positions of the feature points
on the image plane; (3) get the Euclidean matrix from the
moving vector and the three-dimensional information for the
points; and (4) rotate and translate the feature points by
using the Euclidean matrix, and render the now points oil the
image plane, The key algorithm of this tracker is to estimate
the Euclidean matrix by using a least square technique based
oil Lie algebra. Thc resulting tracker performed very well on
the task of tracking a human face.
- Chung, FL, and Ip, WWS, "Complex character decomposition using deformable model," IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C- APPLICATIONS AND REVIEWS, vol. 31, pp. 126-132, 2001.
Abstract:
Despite the facts that Chinese characters are composed of
radicals and Chinese usually formulate their knowledge of
Chinese characters as a combination of radicals, very few
studies have focused on a character decomposition approach to
recognition, i.e., recognizing a character by first extracting
and recognizing its radicals. In this paper, such an approach
is adopted and the problem of how to extract radical subimages
from character images is addressed by proposing an algorithm
based on a deformable model (DM). The application of a DM to
complex character decomposition (and recognition) is a novel
one and concepts like goodness of character decomposition have
been exploited to formulate appropriate energy terms and to
devise cost effective minimization schemes for the problem. The
advantage of the character decomposition approach is
demonstrated by feeding the extracted radical images to an
existing structure-based Chinese character recognizer, the
outputs of which are then combined to classify the input.
Simulation results show that the performance of the existing
system can be improved significantly when character
decomposition is used.
- Mitchell, SC, Lelieveldt, BPF, van der Geest, RJ, Bosch, HG, Reiber, JHC, and Sonka, M, "Multistage hybrid active appearance model matching: Segmentation of left and right ventricles in cardiac MR images," IEEE TRANSACTIONS ON MEDICAL IMAGING, vol. 20, pp. 415-423, 2001.
Abstract:
A fully automated approach to segmentation of the left and
right cardiac ventricles from magnetic resonance (MR) images is
reported. A novel multistage hybrid appearance model
methodology is presented in which a hybrid active shape
model/active appearance model (AAM) stage helps avoid local
minima of the matching function. This yields an overall more
favorable matching result, An automated initialization method
is introduced making the approach fully automated. Our method
was trained in a set of 102 MR images and tested in a separate
set of 60 images. In all testing cases, the matching resulted
in a visually plausible and accurate mapping of the model to
the image data, Average signed border positioning errors did
not exceed 0.3 mm in any of the three determined contours-left-
ventricular (LV) epicardium, LV and right-ventricular (RV)
endocardium. The area measurements derived from the three
contours correlated well with the independent standard (r =
0.96, 0.96, 0.90), with slopes and intercepts of the regression
lines close to one and zero, respectively. Testing the
reproducibility of the method demonstrated an unbiased
performance with small range of error as assessed via Bland-
Altman statistic. In direct border positioning error
comparison, the multistage method significantly outperformed
the conventional AAM (p < 0.001), The developed method promises
to facilitate fully automated quantitative analysis of LV and
RV morphology and function in clinical setting.
- Chang, JY, and Chen, JL, "Automated facial expression recognition system using neural networks," JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, vol. 24, pp. 345-356, 2001.
Abstract:
This paper proposes an automated facial expression recognition
system using neural network classifiers. First, we use the
rough contour. estimation routine, mathematical morphology, and
point contour detection method to extract the precise contours
of the eyebrows, eyes, and mouth of a face image. Then we
define 30 facial characteristic points to describe the position
and shape of these three facial features. Facial expressions
can be described by combining different action units, which are
specified by the basic muscle movements of a human face. We
choose six main action units, composed of facial characteristic
point movements, as the input vectors of two different neural
network-based expression classifiers including a radial basis
function network and a multilayer perceptron network. Using
these two networks, we have obtained recognition rates as high
as 92.1 % in categorizing the facial expressions neutral,
anger, or happiness. Simulation results by the computer
demonstrate that computers are capable of extracting high-level
or abstract information like humans.
- Cootes, TF, Edwards, GJ, and Taylor, CJ, "Active appearance models," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 23, pp. 681-685, 2001.
Abstract:
We describe a new method of matching statistical models of
appearance to images. A set of model parameters control modes
of shape and gray-level variation learned from a training set.
We construct an efficient iterative matching algorithm by
learning the relationship between perturbations in the model
parameters and the induced image errors.
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- Sanchez, G, Llados, J, and Tombre, K, "A mean string algorithm to compute a set of 2D shapes the average among," PATTERN RECOGNITION LETTERS, vol. 23, pp. 203-213, 2002.
Abstract:
An algorithm to compute the mean shape, when the shape is
represented by a string, is presented as a modification of the
well-known string edit algorithm. Given N strings of symbols, a
string edit sequence defines a mapping between their
corresponding symbols. We transform these sets of mapped
symbols (edges) into piecewise linear functions and we compute
their mean. To transform them into functions, we use the
equation of the line defining their edges, and the percentage
of their length, in order to have a common parameterization.
The algorithm has been experimentally tested in the computation
of a representative among a class of shapes in a clustering
procedure in the domain of a graphics recognition application.
(C) 2002 Elsevier Science B.V. All rights reserved.
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