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 ProceedingsAuthor: Edwin R. Hancock, Marcello Pelillo Published by Springer Berlin Heidelberg ISBN: 978-3-540-66294-5 DOI: 10.1007/3-540-48432-9Table of Contents:A Hamiltonian Approach to the Eikonal Equation
Topographic Surface Structure from 2D Images Using Shape-from-Shading
Harmonic Shape Images: A Representation for 3D Free-Form Surfaces Based on Energy Minimization
Deformation Energy for Size Functions
On Fitting Mixture Models
Bayesian Models for Finding and Grouping Junctions
Semi-iterative Inferences with Hierarchical Energy-Based Models for Image Analysis
Metropolis vs Kawasaki Dynamic for Image Segmentation Based on Gibbs Models
Hyperparameter Estimation for Satellite Image Restoration by a MCMCML Method
Auxiliary Variables for Markov Random Fields with Higher Order Interactions
Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Model
Adaptive Bayesian Contour Estimation: A Vector Space Representation Approach
Adaptive Pixel-Based Data Fusion for Boundary Detection
Bayesian A* Tree Search with Expected O(N) Convergence Rates for Road Tracking
A New Algorithm for Energy Minimization with Discontinuities
Convergence of a Hill Climbing Genetic Algorithm for Graph Matching
A New Distance Measure for Non-rigid Image Matching
Continuous-Time Relaxation Labeling Processes
Realistic Animation Using Extended Adaptive Mesh for Model Based Coding
Maximum Likelihood Inference of 3D Structure from Image Sequences