M a T Figueiredo

Energy Minimization Methods in Computer Vision and Pattern Recognition: Third International Workshop, Emmcvpr 2001, Sophia Antipolis France, September 3-5, 2001 Proceedings - Lecture Notes in Computer Science M a T Figueiredo

Marc Notes: Includes bibliographical references and index. Table of Contents: Probabilistic Models and Estimation.- A Double-Loop Algorithm to Minimize the Bethe Free Energy.- A Variational Approach to Maximum a Posteriori Estimation for Image Denoising.- Maximum Likelihood Estimation of the Template of a Rigid Moving Object.- Metric Similarities Learning through Examples: An Application to Shape Retrieval.- A Fast MAP Algorithm for 3D Ultrasound.- Designing the Minimal Structure of Hidden Markov Model by Bisimulation.- Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields.- Matching Images to Models Camera Calibration for 3-D Surface Reconstruction.- A Hierarchical Markov Random Field Model for Figure-Ground Segregation.- Articulated Object Tracking via a Genetic Algorithm.- Image Modelling and Synthesis.- Learning Matrix Space Image Representations.- Supervised Texture Segmentation by Maximising Conditional Likelihood.- Designing Moire Patterns.- Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods.- Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions.- Clustering, Grouping, and Segmentation.- Path Based Pairwise Data Clustering with Application to Texture Segmentation.- A Maximum Likelihood Framework for Grouping and Segmentation.- Image Labeling and Grouping by Minimizing Linear Functionals over Cones.- Grouping with Directed Relationships.- Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model.- Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing.- Edge Based Probabilistic Relaxation for Sub-pixel Contour Extraction.- Two Variational Models for Multispectral Image Classification.- Optimization and Graphs.- An Experimental Comparison of Min-cut/Max-flow Algorithms for Energy Minimization in Vision.- A Discrete/Continuous Minimization Method in Interferometric Image Processing.- Global Energy Minimization: A Transformation Approach.- Global Feedforward Neural Network Learning for Classification and Regression.- Matching Free Trees, Maximal Cliques, and Monotone Game Dynamics.- Efficiently Computing Weighted Tree Edit Distance Using Relaxation Labeling.- Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems.- A Complementary Pivoting Approach to Graph Matching.- Application of Genetic Algorithms to 3-D Shape Reconstruction in an Active Stereo Vision System.- Shapes, Curves, Surfaces, and Templates.- A Markov Process Using Curvature for Filtering Curve Images.- Geodesic Interpolating Splines.- Averaged Template Matching Equations.- A Continuous Shape Descriptor by Orientation Diffusion.- Multiple Contour Finding and Perceptual Grouping as a Set of Energy Minimizing Paths.- Shape Tracking Using Centroid-Based Methods.- Optical Flow and Image Registration: A New Local Rigidity Approach for Global Minimization.- Spherical Object Reconstruction Using Star-Shaped Simplex Meshes.- Gabor Feature Space Diffusion via the Minimal Weighted Area Method.- 3D Flux Maximizing Flows."Publisher Marketing: These are the refereed proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2001, held in Sophia Antipolis, France in September 2001. The 42 revised full papers presented were carefully reviewed and selected from 70 submissions. The book offers topical sections on probabilistic models and estimation image modeling and synthesis clustering, grouping, and segmentation optimization and graphs and shapes, curves, surfaces, and templates.