Energy Minimization Methods in Computer Vision and Pattern Recognition

Energy Minimization Methods in Computer Vision and Pattern Recognition

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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.qGeodesic path between the cat and the lion, with the local rate of dissipation on the shapes S0 , ..., S Kaˆ’1 color-coded as (top) and a ... one arm and one leg of S0 (left) are occluded. to the submanifold of2D area or 3D volume preserving objects based on apredictorcorrectorscheme. ... Thisrequires a minormodification of our model, i.e. solely for k =0inEIm match we insert a smooth function as a mask for S0.

Title:Energy Minimization Methods in Computer Vision and Pattern Recognition
Author:Daniel Cremers, Yuri Boykov, Andrew Blake
Publisher:Springer Science & Business Media - 2009-08-11


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