Energy Minimization Methods in Computer Vision and Pattern Recognition: 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009, ProceedingsDaniel Cremers, Yuri Boykov, Andrew Blake, Frank R. Schmidt Overthelastdecades, energyminimizationmethods havebecomeanestablished paradigm to resolve a variety of challenges in the ?elds of computer vision and pattern recognition. While traditional approaches to computer vision were often based on a heuristic sequence of processing steps and merely allowed very l- ited theoretical understanding of the respective methods, most state-of-the-art methods are nowadays based on the concept of computing solutions to a given problem by minimizing respective energies. This volume contains the papers presented at the 7th International Conf- ence on Energy Minimization Methods in Computer Vision and Pattern Rec- nition (EMMCVPR 2009), held at the University of Bonn, Germany, August 24-28, 2009. These papers demonstrate that energy minimization methods have become a mature ?eld of research spanning a broad range of areas from discrete graph theoretic approaches and Markov random ?elds to variational methods and partial di'erential equations. Application areas include image segmentation and tracking, shape optimization and registration, inpainting and image deno- ing, color and texture modeling, statistics and learning. Overall, we received 75 high-quality double-blind submissions. Based on the reviewer recommendations, 36paperswereselectedforpublication,18asoraland18asposterpresentations. Both oral and poster papers were attributed the same number of pages in the conference proceedings. Furthermore, we were delighted that three leading experts from the ?elds of computer vision and energy minimization, namely, Richard Hartley (C- berra, Australia), Joachim Weickert (Saarbruc ] ken, Germany) and Guillermo Sapiro(Minneapolis, USA)agreedtofurtherenrichtheconferencewithinspiring keynote lectures. |
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Contents
Discrete Optimization and Markov Random Fields | 1 |
Detection and Segmentation of Independently Moving Objects from | 14 |
Efficient Global Minimization for the Multiphase ChanVese Model | 28 |
Bipartite Graph Matching Computation on GPU | 42 |
PoseInvariant Face Matching Using MRF Energy Minimization | 56 |
Parallel Hidden Hierarchical Fields for Multiscale Reconstruction | 70 |
General Search Algorithms for Energy Minimization Problems | 84 |
Partial Differential Equations | 98 |
Intrinsic SecondOrder Geometric Optimization for Robust Point | 274 |
Geodesics in Shape Space via Variational Time Discretization | 288 |
HyperDemons | 303 |
A Structural Decomposition Approach | 317 |
Inpainting and Image Denoising | 331 |
A Variational Framework for Nonlocal Image Inpainting | 345 |
Image Filtering Driven by Level Curves | 359 |
Color Image Restoration Using Nonlocal MumfordShah Regularizers | 373 |
A PDE Approach to Coupled SuperResolution with Nonparametric | 112 |
On a Decomposition Model for Optical Flow | 126 |
Computing the Local Continuity Order of Optical Flow Using | 154 |
A Local NormalBased Region Term for Active Contours | 168 |
Segmentation and Tracking | 182 |
Complementary Optic Flow | 207 |
Parameter Estimation for Marked Point Processes Application | 221 |
Three Dimensional Monocular Human Motion Analysis in EndEffector | 235 |
Robust Segmentation by Cutting across a Stack of Gamma Transformed | 249 |
Reconstructing Optical Flow Fields by Motion Inpainting | 388 |
Color and Texture | 401 |
QuaternionBased Color Image Smoothing Using a Spatially Varying | 415 |
Locally Parallel Textures Modeling with Adapted Hilbert Spaces | 429 |
Global Optimal Multiple Object Detection Using the Fusion of Shape | 443 |
ClusteringBased Construction of Hidden Markov Models | 466 |
Boundaries as Contours of Optimal Appearance and Area of Support | 480 |
Author Index | 493 |
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Common terms and phrases
algorithm Analysis applied approach approximation assume boundary color image compared complex Computer Vision consider continuous contours corresponding defined deformation denotes derived detection different diffusion discrete distance distribution edges energy equation estimation evaluation experiments extract field Figure first flow formulation frame framework function given global gradient graph Heidelberg IEEE IEEE Trans initial inpainting intensity introduced iteration labels linear LNCS matching matrix mean measure method metric minimization motion move node noise norm normal object obtained optical optimal parameters patch Pattern performance pixels presented prior problem Proc Processing proposed quaternion range Recognition reconstruction References region regularization representation respect samples scale scheme segmentation sequence shape similar smooth solution space Springer step structure term texture tion transformation variational vector weights
Bibliographic information
| Title | Energy Minimization Methods in Computer Vision and Pattern Recognition: 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009, Proceedings Volume 5681 of Image Processing, Computer Vision, Pattern Recognition, and Graphics Volume 5681 of Lecture Notes in Computer Science |
| Editors | Daniel Cremers, Yuri Boykov, Andrew Blake, Frank R. Schmidt |
| Edition | illustrated |
| Publisher | Springer Science & Business Media, 2009 |
| ISBN | 3642036406, 9783642036408 |
| Length | 494 pages |
| Subjects | › › Computers / Artificial Intelligence / Computer Vision & Pattern Recognition Computers / Data Science / Data Analytics Computers / Hardware / General Computers / Optical Data Processing Computers / Programming / Algorithms Computers / Software Development & Engineering / Computer Graphics Computers / Software Development & Engineering / General Computers / Software Development & Engineering / Systems Analysis & Design |
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