A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image restoration using an estimated Markov model
Signal Processing
Hands: a pattern theoretic study of biological shapes
Hands: a pattern theoretic study of biological shapes
Comparing sweep strategies for stochastic relaxation
Journal of Multivariate Analysis
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical Markov modeling approach for the segmentation and tracking of deformable shapes
Graphical Models and Image Processing
Deformable template models: a review
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Globally constrained deformable models for 3D object reconstruction
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Optimal Bayesian Estimators For Image Segmentation and Surface Reconstruction
Optimal Bayesian Estimators For Image Segmentation and Surface Reconstruction
Animating Chinese paintings through stroke-based decomposition
ACM Transactions on Graphics (TOG)
Image Segmentation Based on Cluster Ensemble
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
An improved time-adaptive self-organizing map for high-speed shape modeling
Pattern Recognition
Quantization-based clustering algorithm
Pattern Recognition
Modelling self-assembly in BlenX
Transactions on Computational Systems Biology XII
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This paper presents a novel approach to shape modeling and a model-based image segmentation procedure tailor-made for the proposed shape model. A common way to represent shape is based on so-called key points and leads to shape variables, which are invariant with respect to similarity transformations. We propose a graphical shape model, which relies on a certain conditional independence structure among the shape variables. Most often, it is sufficient to use a sparse underlying graph reflecting both nearby and long-distance key point interactions. Graphical shape models allow for specific shape modeling, since, e.g., for the subclass of decomposable graphical Gaussian models both model selection procedures and explicit parameter estimates are available. A further prerequisite to a successful application of graphical shape models in image analysis is provided by the 驴toolbox驴 of Markov chain Monte Carlo methods offering highly flexible and effective methods for the exploration of a specified distribution. For Bayesian image segmentation based on a graphical Gaussian shape model, we suggest applying a hybrid approach composed of the well-known Gibbs sampler and the more recent slice sampler. Shape modeling as well as image analysis are demonstrated for the segmentation of vertebrae from two-dimensional slices of computer tomography images.