Energy minimization methods in computer vision and pattern recognition

Energy Minimization Methods in Computer Vision and Pattern Recognition: Second International Workshop, EMMCVPR’99 York, UK, July 26–29, 1999 Proceedings<br />Author: Edwin R. Hancock, Marcello Pelillo<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-66294-5<br /> DOI: 10.1007/3-540-48432-9<br /><br />Table of Contents:<p></p><ul><li>A Hamiltonian Approach to the Eikonal Equation </li><li>Topographic Surface Structure from 2D Images Using Shape-from-Shading </li><li>Harmonic Shape Images: A Representation for 3D Free-Form Surfaces Based on Energy Minimization </li><li>Deformation Energy for Size Functions </li><li>On Fitting Mixture Models </li><li>Bayesian Models for Finding and Grouping Junctions </li><li>Semi-iterative Inferences with Hierarchical Energy-Based Models for Image Analysis </li><li>Metropolis vs Kawasaki Dynamic for Image Segmentation Based on Gibbs Models </li><li>Hyperparameter Estimation for Satellite Image Restoration by a MCMCML Method </li><li>Auxiliary Variables for Markov Random Fields with Higher Order Interactions </li><li>Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Model </li><li>Adaptive Bayesian Conto