Energy Minimization Methods in Computer Vision and Pattern Recognition（计算机视觉与模式识别中的能量最小化方法）

The LNCS series reports state-of-the-art results in computer science research,development,and education,at a high level and in both printed and electronic form.Enjoying tight cooperation with the R&D community,with numerous individuals,as well as with prestigious organizations and societies,LNCS has grown into the most comprehensive computer science resarch forum available.
The scope of LNCS,including its subseries LNAI,spans the whole range of computer science and information technology including interdisciplinary topics in a variety of application fields.The type of material publised traditionally includes.
-proceedings(published in time for the respective conference)
-post-proceedings(consisting of thoroughly revised final full papers)
-research monographs(which may be basde on outstanding PhD work,research projects,technical reports,etc.)

在线阅读本书

This book constitutes 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.

Ⅰ 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 Examplesg 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
Ⅱ Image Modelling and Synthesis
Learning Matrix Space Image Representations
Supervised Texture Segmentation by Maximising Conditional Likelihood
Designing Moir6 Patterns
Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods
Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions
Ⅲ 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
Ⅳ Optimization and Graphs
Ⅴ Shapes,Curves,Surfaces,and Templates
Author Index