New PDF release: Computer Vision – ECCV 2012: 12th European Conference on

By Dhruv Batra, Payman Yadollahpour, Abner Guzman-Rivera, Gregory Shakhnarovich (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

ISBN-10: 3642337147

ISBN-13: 9783642337147

ISBN-10: 3642337155

ISBN-13: 9783642337154

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed court cases of the twelfth eu convention on desktop imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers offered have been rigorously reviewed and chosen from 1437 submissions. The papers are prepared in topical sections on geometry, 2nd and 3D shapes, 3D reconstruction, visible acceptance and type, visible beneficial properties and photo matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and snapshot registration, photometry: lighting fixtures and color, and photo segmentation.

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Extra info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V

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ECCV 2012, Part V, LNCS 7576, pp. 31–44, 2012. c Springer-Verlag Berlin Heidelberg 2012 32 V. Vineet, J. S. Torr which permit substantially faster inference in these problems, as well as offering performance gains over competing methods. Our approach builds on such filterbased approaches and shows them to outperform or perform equally well to the previously dominant graph-cut/move-making approaches on all problems considered. This strongly suggests that mean-field message-passing enhanced with recent filtering techniques should be considered as a general state-of-the-art inference method for a large number of computer vision problems currently of interest.

255–262 (2011) 14. : On quadratization of pseudo-boolean functions. In: International Symposium on Artificial Intelligence and Mathematics (2012) 15. : A graph cut algorithm for higher-order markov random fields. In: IEEE International Conference on Computer Vision, pp. 1020–1027 (2011) 16. : Energy minimization via graph cuts: settling what is possible. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 939–946 (2005) 17. : What energy functions can be minimized via graph cuts? : Classes of submodular constraints expressible by graph 18.

However, not only reduction techniques do not preserve submodularity, not all higher order submodular functions can be reduced to quadratic submodular forms [18]. – Ishikawa [4] has noted that reduction of the higher order polynomial form to quadratic, for a k-clique, adds 2k auxiliary variables per clique making this approach virtually unusable even for moderate sized cliques. – QPBO algorithm when applied to reduced quadratic forms leaves many nodes unlabeled. This can happen even if the original pseudo Boolean function is submodular.

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Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part V by Dhruv Batra, Payman Yadollahpour, Abner Guzman-Rivera, Gregory Shakhnarovich (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)


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