Energy Minimization Methods in Computer Vision and Pattern Recognition: International Workshop EMMCVPR'97, Venice, Italy, May 21-23, 1997, Proceedings [Paperback]

This book constitutes the refereed proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR'97, held in Venice, Italy, in May 1997.
The book presents 29 revised full papers selected from a total of 62 submissions. Also included are four full invited papers and a keynote paper by leading researchers. The volume is organized in sections on contours and deformable models, Markov random fields, deterministic methods, object recognition, evolutionary search, structural models, and applications. The volume is the first comprehensive documentation of the application of energy minimization techniques in the areas of compiler vision and pattern recognition.Reliable computation and related games.- Characterizing the distribution of completion shapes with corners using a mixture of random processes.- Adaptive parametrically deformable contours.- Kona: A multi-junction detector using minimum description length principle.- Restoration of SAR images using recovery of discontinuities and non-linear optimization.- Geometrically deformable templates for shape-based segmentation and tracking in cardiac MR images.- Image segmentation via energy minimization on partitions with connected components.- Restoration of severely blurred high range images using stochastic and deterministic relaxation algorithms in compound gauss Markov random fields.- Maximum likelihood estimation of Markov Random Field parameters using Markov Chain Monte Carlo algorithms.- Noniterative manipulation of discrete energy-based models for image analysis.- Unsupervised image segmentation using Markov Random Field models.- Adaptive anisotropic parameter estimation in the weak membrane model.- Twenty questions, focus of attention, and A*: A theoretical comparison of optimization strategies.- Deterministic annealing for unsupervised texture segmentation.- Self annealing: Unifying deterministic annealing and relaxation labeling.- Multidimensilc�