Energy Minimization Methods in Computer Vision and Pattern Recognition

1. Front Matter

   Pages I-X

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2. Probabilistic Models and Estimation

   1. A Double-Loop Algorithm to Minimize the Bethe Free Energy

      • Alan Yuille

      Pages 3-18

   2. A Variational Approach to Maximum a Posteriori Estimation for Image Denoising

      • A. Ben Hamza, Hamid Krim

      Pages 19-34

   3. Maximum Likelihood Estimation of the Template of a Rigid Moving Object

      • Pedro M. Q. Aguiar, José M. F. Moura

      Pages 34-49

   4. Metric Similarities Learning through Examples: An Application to Shape Retrieval

      • Alain Trouvé, Yong Yu

      Pages 50-62

   5. A Fast MAP Algorithm for 3D Ultrasound

      • João M. Sanches, Jorge S. Marques

      Pages 63-74

   6. Designing the Minimal Structure of Hidden Markov Model by Bisimulation

      • Manuele Bicego, Agostino Dovier, Vittorio Murino

      Pages 75-90

   7. Relaxing Symmetric Multiple Windows Stereo Using Markov Random Fields

      • Andrea Fusiello, Umberto Castellani, Vittorio Murino

      Pages 91-105

   8. Matching Images to Models — Camera Calibration for 3-D Surface Reconstruction

      • Robin D. Morris, Vadim N. Smelyanskiy, Peter C. Cheeseman

      Pages 105-117

   9. A Hierarchical Markov Random Field Model for Figure-Ground Segregation

      • Stella X. Yu, Tai Sing Lee, Takeo Kanade

      Pages 118-133

   10. Articulated Object Tracking via a Genetic Algorithm

       • Jairo Rocha, Arnau Mir

       Pages 134-149

3. Image Modelling and Synthesis

   1. Learning Matrix Space Image Representations

      • Anand Rangarajan

      Pages 153-168

   2. Supervised Texture Segmentation by Maximising Conditional Likelihood

      • Georgy Gimel’farb

      Pages 169-184

   3. Designing Moiré Patterns

      • Guy Lebanon, Alfred M. Bruckstein

      Pages 185-200

   4. Optimization of Paintbrush Rendering of Images by Dynamic MCMC Methods

      • Tamás Szirányi, Zoltán Tóth

      Pages 201-215

   5. Illumination Invariant Recognition of Color Texture Using Correlation and Covariance Functions

      • Mohammed Al-Rawi, Yang Jie

      Pages 216-231

4. Clustering, Grouping, and Segmentation

   1. Path Based Pairwise Data Clustering with Application to Texture Segmentation

      • Bernd Fischer, Thomas Zöller, Joachim M. Buhmann

      Pages 235-250

   2. A Maximum Likelihood Framework for Grouping and Segmentation

      • Antonio Robles-Kelly, Edwin R. Hancock

      Pages 251-267

   3. Image Labeling and Grouping by Minimizing Linear Functionals over Cones

      • Christian Schellewald, Jens Keuchel, Christoph Schnörr

      Pages 267-282

   4. Grouping with Directed Relationships

      • Stella X. Yu, Jianbo Shi

      Pages 283-297

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”.

• Computer Vision
• Energy minimization
• Markov random fields
• clustering
• hidden Markov models
• image classification
• modeling
• neural networks
• optical flow
• optimization
• pattern recognition
• rendering
• simulated annealing
• texture segmentations
• algorithm analysis and problem complexity

• Instituto de Telecomunicacoes and Instituto Superior Tecnico, Lisboa, Portugal

  Mário Figueiredo

• INRIA, France

  Josiane Zerubia

• Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

  Anil K. Jain

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