Energy Minimization Methods in Computer Vision and Pattern Recognition [electronic resource] :Third International Workshop, EMMCVPR 2001 Sophia Antipolis, France, September 3–5, 2001 Proceedings /
Contributor(s): Figueiredo, Mário [editor.] | Zerubia, Josiane [editor.] | Jain, Anil K [editor.] | SpringerLink (Online service).Material type: BookSeries: Lecture Notes in Computer Science: 2134Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2001.Description: X, 652 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540447450.Subject(s): Computer science | Computers | Algorithms | Artificial intelligence | Computer graphics | Image processing | Pattern recognition | Computer Science | Pattern Recognition | Image Processing and Computer Vision | Computer Graphics | Artificial Intelligence (incl. Robotics) | Algorithm Analysis and Problem Complexity | Computation by Abstract DevicesOnline resources: Click here to access online
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 Moiré 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.
This volume consists of the 42 papers presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2001),whichwasheldatINRIA(InstitutNationaldeRechercheen Informatique et en Automatique) in Sophia Antipolis, France, from September 3 through September 5, 2001. This workshop is the third of a series, which was started with EMMCVPR’97, held in Venice in May 1997, and continued with EMMCVR’99, which took place in York, in July 1999. Minimization problems and optimization methods permeate computer vision (CV), pattern recognition (PR), and many other ?elds of machine intelligence. The aim of the EMMCVPR workshops is to bring together people with research interests in this interdisciplinary topic. Although the subject is traditionally well represented at major international conferences on CV and PR, the EMMCVPR workshops provide a forum where researchers can report their recent work and engage in more informal discussions. We received 70 submissions from 23 countries, which were reviewed by the members of the program committee. Based on the reviews, 24 papers were - cepted for oral presentation and 18 for poster presentation. In this volume, no distinction is made between papers that were presented orally or as posters. The book is organized into ?ve sections, whose topics coincide with the ?ve s- sionsoftheworkshop:“ProbabilisticModelsandEstimation”,“ImageModelling and Synthesis”, “Clustering, Grouping, and Segmentation”, “Optimization and Graphs”, and “Shapes, Curves, Surfaces, and Templates”.