Energy Minimization Methods in Computer Vision and Pattern Recognition: 7th International Conference, EMMCVPR 2009, Bonn, Germany, August 24-27, 2009, Proceedings

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Daniel Cremers, Yuri Boykov, Andrew Blake, Frank R. Schmidt
Springer Science & Business Media, Aug 11, 2009 - Computers - 494 pages
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

Multilabel Moves for MRFs with Truncated Convex Priors
1
Detection and Segmentation of Independently Moving Objects from Dense Scene Flow
14
Efficient Global Minimization for the Multiphase ChanVese Model of Image Segmentation
28
Bipartite Graph Matching Computation on GPU
42
PoseInvariant Face Matching Using MRF Energy Minimization Framework
56
Parallel Hidden Hierarchical Fields for Multiscale Reconstruction
70
General Search Algorithms for Energy Minimization Problems
84
Complex Diffusion on Scalar and Vector Valued Image Graphs
98
Integrating the Normal Field of a Surface in the Presence of Discontinuities
261
Intrinsic SecondOrder Geometric Optimization for Robust Point Set Registration without Correspondence
274
Geodesics in Shape Space via Variational Time Discretization
288
HyperDemons Algorithm
303
A Structural Decomposition Approach for Image Analysis
317
ExemplarBased Interpolation of Sparsely Sampled Images
331
A Variational Framework for Nonlocal Image Inpainting
345
Image Filtering Driven by Level Curves
359

A PDE Approach to Coupled SuperResolution with Nonparametric Motion
112
On a Decomposition Model for Optical Flow
126
Application to Image Analysis
140
Computing the Local Continuity Order of Optical Flow Using Fractional Variational Method
154
A Local NormalBased Region Term for Active Contours
168
Hierarchical Pairwise Segmentation Using Dominant Sets and Anisotropic Diffusion Kernels
182
Tracking as Segmentation of SpatialTemporal Volumes by Anisotropic Weighted TV
193
Complementary Optic Flow
207
Parameter Estimation for Marked Point Processes Application to Object Extraction from Remote Sensing Images
221
Three Dimensional Monocular Human Motion Analysis in EndEffector Space
235
Robust Segmentation by Cutting across a Stack of Gamma Transformed Images
249
Color Image Restoration Using Nonlocal MumfordShah Regularizers
373
Reconstructing Optical Flow Fields by Motion Inpainting
388
Color Image Segmentation in a Quaternion Framework
401
QuaternionBased Color Image Smoothing Using a Spatially Varying Kernel
415
Locally Parallel Textures Modeling with Adapted Hilbert Spaces
429
Global Optimal Multiple Object Detection Using the Fusion of Shape and Color Information
443
Human Age Estimation by Metric Learning for Regression Problems
455
ClusteringBased Construction of Hidden Markov Models for Generative Kernels
466
Boundaries as Contours of Optimal Appearance and Area of Support
480
Author Index
492
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