Energy Minimization Methods in Computer Vision and Pattern Recognition

6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007. Proceedings

  • Editors
  • Alan L. Yuille
  • Song-Chun Zhu
  • Daniel Cremers
  • Yongtian Wang
Conference proceedings EMMCVPR 2007

DOI: 10.1007/978-3-540-74198-5

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4679)

Table of contents

  1. Front Matter
  2. Algorithms

    1. Frank R. Schmidt, Eno Töppe, Daniel Cremers, Yuri Boykov
      Pages 39-54
    2. Yu Liu, Olga Veksler, Olivier Juan
      Pages 55-70
    3. Zhouyu Fu, Antonio Robles-Kelly
      Pages 71-86
  3. Applications to Faces and Text

    1. Dashan Gao, Yizhou Wang
      Pages 97-111
    2. You-Wei Wen, Michael Ng, Wai-ki Ching
      Pages 112-126
    3. Feng-ying Xie, Zhi-guo Jiang, Lei Wang
      Pages 127-136
    4. Xin Yu, Jinwen Tian, Jian Liu
      Pages 137-144
    5. Xiangqian Wu, Kuanquan Wang, David Zhang, Ning Qi
      Pages 145-152
  4. Image Parsing

    1. Praveen Srinivasan, Jianbo Shi
      Pages 153-168
    2. Feng Min, Jin-Li Suo, Song-Chun Zhu, Nong Sang
      Pages 184-197
    3. Shaowu Peng, Liang Lin, Jake Porway, Nong Sang, Song-Chun Zhu
      Pages 198-212
    4. Ru-Xin Gao, Tian-Fu Wu, Song-Chun Zhu, Nong Sang
      Pages 213-224
  5. Image Processing

    1. Yun-Chung Chung, Shyang-Lih Chang, Shen Cherng, Sei-Wang Chen
      Pages 225-241

About these proceedings

Introduction

This volume contains the papers presented at the Sixth International Conference on Energy Minimization Methods on Computer Vision and Pattern Recognition (EMMCVPR 2007), held at the Lotus Hill Institute, Ezhou, Hubei, China, August 27–29, 2007. The motivation for this conference is the realization that many problems in computer vision and pattern recognition can be formulated in terms of probabilistic inference or optimization of energy functions. EMMCVPR 2007 addressed the critical issues of representation, learning, and inference. Important new themes include pr- abilistic grammars, image parsing, and the use of datasets with ground-truth to act as benchmarks for evaluating algorithms and as a way to train learning algorithms. Other themes include the development of efficient inference algorithms using advanced techniques from statistics, computer science, and applied mathematics. We received 140 submissions for this workshop. Each paper was reviewed by three committee members. Based on these reviews we selected 22 papers for oral presen- tion and 15 papers for poster presentation. This book makes no distinction between oral and poster papers. We have organized these papers in seven sections on al- rithms, applications, image parsing, image processing, motion, shape, and thr- dimensional processing. Finally, we thank those people who helped make this workshop happen. We - knowledge the Program Committee for their help in reviewing the papers.

Keywords

Computer Vision Gabor filter algorithmic learning algorithms clustering cognition data mining energy minimization feature extraction geometric computing graph matching image analysis image classification image processing reflection

Bibliographic information

  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-74195-4
  • Online ISBN 978-3-540-74198-5
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349