Energy Minimization Methods in Computer Vision and Pattern Recognition

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

500 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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.

• Reihe: Lecture Notes in Computer Science
• Themen: Informatik und Informationstechnologie Informatik Künstliche Intelligenz (KI) Maschinelles Sehen, Bildverstehen
• ISBN: 9783540741954
• Sprache: Englisch
• Erscheinungsdatum: 15.08.2007
• Größe: 23.5 x 15.5 cm
• Verlag: Springer Berlin
• Herausgegeben von: Alan L. Yuille, Song-Chun Zhu, Daniel Cremers, Yongtian Wang