Computer Vision – ACCV 2012: 11th Asian Conference on by Michael Stoll, Sebastian Volz, Andrés Bruhn (auth.), Kyoung

By Michael Stoll, Sebastian Volz, Andrés Bruhn (auth.), Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu (eds.)

The four-volume set LNCS 7724--7727 constitutes the completely refereed post-conference complaints of the eleventh Asian convention on computing device imaginative and prescient, ACCV 2012, held in Daejeon, Korea, in November 2012. the complete of 226 contributions offered in those volumes used to be rigorously reviewed and chosen from 869 submissions. The papers are prepared in topical sections on item detection, studying and matching; item acceptance; function, illustration, and popularity; segmentation, grouping, and class; picture illustration; snapshot and video retrieval and scientific photograph research; face and gesture research and popularity; optical move and monitoring; movement, monitoring, and computational images; video research and motion attractiveness; form reconstruction and optimization; form from X and photometry; functions of computing device imaginative and prescient; low-level imaginative and prescient and functions of computing device vision.

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Extra info for Computer Vision – ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part III

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In: Proc. IEEE International Conference on Computer Vision, pp. 1583–1590 (2011) 18. : Fast cost-volume filtering for visual correspondence and beyond. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3017–3024 (2011) 19. : Large displacement optical flow computation without warping. In: Proc. IEEE International Conference on Computer Vision (2009) 20. : Layered image motion with explicit occlusions, temporal consistency, and depth ordering. In: Proc. Advances in Neural Information Processing Systems 23, pp.

Bruhn In our second experiment, we investigate the impact of incorporating feature matches for sequences that do not contain large displacements. To this end, we evaluated our method on the Middlebury training data set and computed the average angular error (AAE). 5, d = 3 ·10−5 , s = 2 ·10−2 ). As one can see in Tab. 1, the results do not deteriorate when incorporating feature matches in our baseline method. In contrast, for some of the sequences we even observe notable improvements although displacements are relatively small (Urban 2, Urban 3, Grove 3).

Stoll, S. Volz, and A. Bruhn 9. : Histogram of oriented gradients for human detection. In: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005) 10. : A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework. In: Proc. ECCV Workshop on Computer Vision with GPUs (2010) 11. : A combined corner and edge detector. In: Proc. Alvey Vision Conference, pp. 147–152 (1988) 12. : Determining optical flow. Artificial Intelligence 17, 185–203 (1981) 13.

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