Energy Minimization Methods in Computer Vision and Pattern Recognition: 6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007, ProceedingsAlan L. Yuille, Song-Chun Zhu, Daniel Cremers, Yongtian Wang 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. |
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Contents
Algorithms | 1 |
Exact Solution of Permuted Submodular MinSum Problems | 28 |
Simulating Classic Mosaics with Graph Cuts | 55 |
An Energy Minimisation Approach to Attributed Graph | 71 |
Applications to Faces and Text | 87 |
Corrected InverseDenoising Filter for Image Restoration | 112 |
Skew Detection Algorithm for Form Document Based on Elongate | 127 |
Combining Left and Right Irises for Personal Authentication | 145 |
Improved Object Tracking Using an Adaptive Colour Model | 280 |
Vehicle Tracking Based on Image Alignment in Aerial Videos | 295 |
Probabilistic Fiber Tracking Using Particle Filtering and | 303 |
Compositional Object Recognition Segmentation and Tracking | 318 |
Bayesian OrderAdaptive Clustering for Video Segmentation | 334 |
Dynamic Feature Cascade for Multiple Object Tracking with | 350 |
Shape Classification Based on Skeleton Path Similarity | 375 |
Shape Analysis of Open Curves in R3 with Applications to Study | 399 |
Image Parsing | 153 |
An Automatic Portrait System Based on AndOr Graph | 184 |
Object Category Recognition Using Generative Template Boosting | 198 |
Bayesian Inference for Layer Representation with Mixed Markov | 213 |
Noise Removal and Restoration Using VotingBased Analysis | 242 |
Motion Analysis | 267 |
ThreeDimensional Processing | 414 |
3D Computation of Gray Level Cooccurrence in Hyperspectral Image | 429 |
A New Bayesian Method for Range Image Segmentation | 453 |
Marked Point Process for Vascular Tree Extraction on Angiogram | 467 |
| 493 | |
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Common terms and phrases
algorithm Analysis applied approach approximate boundary candidate cluster color components compositions Computer Vision connected considered contour corresponding curves defined denote described detection different diffuse direction distance distribution document edge energy error estimation example experiments extracted face fiber Figure filter first frame framework function given graph cut IEEE initial input International introduced iteration labeling layer learning Machine matching mean measure method minimization node noise normal object observed obtained occlusion optimal orientation original pairs parameters parses partition path Pattern performance pixels points presented probability problem projection proposed Recognition reconstruction region regularization representation represented respectively sampling segmentation selected separate shape shown similar skeleton sketch space specular statistical step structure surface task tensor texture tile tion tracking University vector vertices weight zone
Bibliographic information
| Title | Energy Minimization Methods in Computer Vision and Pattern Recognition: 6th International Conference, EMMCVPR 2007, Ezhou, China, August 27-29, 2007, Proceedings Image Processing, Computer Vision, Pattern Recognition, and Graphics Lecture Notes in Computer Science |
| Editors | Alan L. Yuille, Song-Chun Zhu, Daniel Cremers, Yongtian Wang |
| Edition | illustrated |
| Publisher | Springer Science & Business Media, 2007 |
| ISBN | 354074195X, 9783540741954 |
| Length | 494 pages |
| Subjects | › › Computers / Artificial Intelligence / Computer Vision & Pattern Recognition Computers / Artificial Intelligence / General Computers / Data Science / Data Analytics Computers / Optical Data Processing Computers / Programming / Algorithms Computers / Software Development & Engineering / Computer Graphics Computers / Software Development & Engineering / General |
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