Results 11  20
of
63
CPM: A deformable model for shape recovery and segmentation based on charged particles
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2004
"... Abstract—A novel, physically motivated deformable model for shape recovery and segmentation is presented. The model, referred to as the chargedparticle model (CPM), is inspired by classical electrodynamics and is based on a simulation of charged particles moving in an electrostatic field. The charg ..."
Abstract

Cited by 35 (4 self)
 Add to MetaCart
(Show Context)
Abstract—A novel, physically motivated deformable model for shape recovery and segmentation is presented. The model, referred to as the chargedparticle model (CPM), is inspired by classical electrodynamics and is based on a simulation of charged particles moving in an electrostatic field. The charges are attracted towards the contours of the objects of interest by an electrostatic field, whose sources are computed based on the gradientmagnitude image. The electric field plays the same role as the potential forces in the snake model, while internal interactions are modeled by repulsive Coulomb forces. We demonstrate the flexibility and potential of the model in a wide variety of settings: shape recovery using manual initialization, automatic segmentation, and skeleton computation. We perform a comparative analysis of the proposed model with the active contour model and show that specific problems of the latter are surmounted by our model. The model is easily extendable to 3D and copes well with noisy images. Index Terms—Deformable model, chargedparticle system, electrostatic field, Coulomb force, segmentation, shape recovery, skeleton.
Brownian Strings: Segmenting Images with Stochastically Deformable Models
, 1995
"... 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 glob ..."
Abstract

Cited by 34 (0 self)
 Add to MetaCart
(Show Context)
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 imagederived “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. Additional information is available from
Bayesian Object Localisation in Images
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 2001
"... A Bayesian approach to intensitybased object localisation is presented that employs a learned probabilistic model of image filterbank output, applied via Monte Carlo methods, to escape the inefficiency of exhaustive search. An adequate ..."
Abstract

Cited by 29 (1 self)
 Add to MetaCart
A Bayesian approach to intensitybased object localisation is presented that employs a learned probabilistic model of image filterbank output, applied via Monte Carlo methods, to escape the inefficiency of exhaustive search. An adequate
Statistical Models of Visual Shape and Motion
 A
, 1998
"... This paper addresses some problems in the interpretation of visually observed shapes in motion, both planar and threedimensional shapes. Mumford (1996), interpreting the "Pattern Theory" developed over a number of years by Grenander (1976), views images as "pure" patterns that h ..."
Abstract

Cited by 26 (0 self)
 Add to MetaCart
(Show Context)
This paper addresses some problems in the interpretation of visually observed shapes in motion, both planar and threedimensional shapes. Mumford (1996), interpreting the "Pattern Theory" developed over a number of years by Grenander (1976), views images as "pure" patterns that have been distorted by a combination of four kinds of degradations. This view applies naturally to the analysis of static, twodimensional images. The four degradations are given here, together with comments on how they need to be extended to take account of threedimensional objects in motion.
Automatic tracking of the aorta in cardiovascular MR images using deformable models
 IEEE Transactions on Medical Imaging
, 1997
"... We present a new algorithm for the robust and accurate tracking of the aorta in cardiovascular MR images. First, a rough estimate of the location and diameter of the aorta is obtained by applying a multiscale medial response function using the available apriori knowledge. Then, this estimate is ref ..."
Abstract

Cited by 23 (1 self)
 Add to MetaCart
(Show Context)
We present a new algorithm for the robust and accurate tracking of the aorta in cardiovascular MR images. First, a rough estimate of the location and diameter of the aorta is obtained by applying a multiscale medial response function using the available apriori knowledge. Then, this estimate is refined using an energyminimizing deformable model which we define in a MarkovRandomField (MRF) framework. In this context we propose a global minimization technique based on stochastic relaxation, Simulated Annealing (SA), which is shown to be superior to other minimization techniques, for minimizing the energy of the deformable model. We have evaluated the performance and robustness of the algorithm on clinical compliance studies in cardiovascular MR images. The segmentation and tracking has been successfully tested in spinecho MR images of the aorta. The results show the ability of the algorithm to produce not only accurate but also very reliable results in clinical routine applications....
Image Sequence Restoration Using Gibbs Distributions
, 1995
"... This thesis addresses a number of issues concerned with the restoration of one type of image sequence, namely archived black and white motion pictures. These are often a valuable historical record, but because of the physical nature of the film they can suffer from a variety of degradations which re ..."
Abstract

Cited by 23 (0 self)
 Add to MetaCart
This thesis addresses a number of issues concerned with the restoration of one type of image sequence, namely archived black and white motion pictures. These are often a valuable historical record, but because of the physical nature of the film they can suffer from a variety of degradations which reduce their usefulness. The main visual defects are `dirt and sparkle' due to dust and dirt becoming attached to the film, or abrasion removing the emulsion, and `line scratches' due to the film running against foreign bodies in the camera or projector. For an image
The Contracting Curve Density Algorithm: Fitting Parametric Curve Models to Images Using Local Selfadapting Separation Criteria
 International Journal of Computer Vision (IJCV
, 2004
"... The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3D reconstruction. In this article, we propose the Contracting Cur ..."
Abstract

Cited by 21 (1 self)
 Add to MetaCart
(Show Context)
The task of fitting parametric curve models to the boundaries of perceptually meaningful image regions is a key problem in computer vision with numerous applications, such as image segmentation, pose estimation, object tracking, and 3D reconstruction. In this article, we propose the Contracting Curve Density (CCD) algorithm as a solution to the curvefitting problem.
Image Segmentation Using Gradient Vector Diffusion and Region Merging
 In Proceedings of International Conference on Pattern Recognition
, 2002
"... Active Contour (or Snake) Model is recognized as one of the efficient tools for 2D/3D image segmentation. However, traditional snake models prove to be limited in several aspects. The present paper describes a set of diffusion equations applied to image gradient vectors, yielding a vector field over ..."
Abstract

Cited by 20 (8 self)
 Add to MetaCart
(Show Context)
Active Contour (or Snake) Model is recognized as one of the efficient tools for 2D/3D image segmentation. However, traditional snake models prove to be limited in several aspects. The present paper describes a set of diffusion equations applied to image gradient vectors, yielding a vector field over the image domain. The obtained vector field provides the Snake Model an external force as well as an automatic way to generate the initial contours. Finally a region merging technique is employed to further improve the segmentation results.
A Metropolis Sampler for Polygonal Image Reconstruction
, 1995
"... We show how a stochastic model of polygonal objects can provide a Bayesian framework for the interpretation of colouring data in the plane. We describe a particular model and give a Markov Chain Monte Carlo (MCMC) algorithm for simulating the posterior distribution of the polygonal pattern. Two impo ..."
Abstract

Cited by 19 (1 self)
 Add to MetaCart
(Show Context)
We show how a stochastic model of polygonal objects can provide a Bayesian framework for the interpretation of colouring data in the plane. We describe a particular model and give a Markov Chain Monte Carlo (MCMC) algorithm for simulating the posterior distribution of the polygonal pattern. Two important observations arise from our implementation of the algorithm. First, it is computationally feasible to use MCMC to simulate the posterior distribution of a polygonal process for moderately large problems (ie, 10000 data points, with polygonal patterns involving around 120 edges). Our implementation, which we would describe as careful, but unsophisticated, produces satisfactory approximations to the mode of the posterior in about 5 minutes on a SUN Sparc 2. Independent samples from the posterior take a few seconds to generate. The second observation is that the Arak process, a particular type of polygonal process, makes a wonderful debugging tool for testing shape simulation software. Th...
Statistical Deformable ModelBased Segmentation Of Image Motion
, 1999
"... We present a statistical method for the motionbased segmentation of deformable structures undergoing nonrigid movements. The proposed approach relies on two models describing the shape of interest, its variability and its movement. The first model corresponds to a statistical deformable templat ..."
Abstract

Cited by 19 (0 self)
 Add to MetaCart
We present a statistical method for the motionbased segmentation of deformable structures undergoing nonrigid movements. The proposed approach relies on two models describing the shape of interest, its variability and its movement. The first model corresponds to a statistical deformable template that constrains the shape and its deformations. The second model is introduced to represent the optical flow field inside the deformable template. These two models are combined within a single probability distribution which enables to derive optimal shape and motion estimates using a Maximum Likelihood approach. The method requires no manual initialization and is demonstrated on synthetic data and on a medical Xray image sequence.