Download Computer Vision – ECCV 2008: 10th European Conference on by Wei Zeng, Yun Zeng, Yang Wang, Xiaotian Yin, Xianfeng Gu, PDF

Download Computer Vision – ECCV 2008: 10th European Conference on by Wei Zeng, Yun Zeng, Yang Wang, Xiaotian Yin, Xianfeng Gu, PDF

By Wei Zeng, Yun Zeng, Yang Wang, Xiaotian Yin, Xianfeng Gu, Dimitris Samaras (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)

The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed complaints of the tenth ecu convention on desktop imaginative and prescient, ECCV 2008, held in Marseille, France, in October 2008.

The 243 revised papers offered have been conscientiously reviewed and chosen from a complete of 871 papers submitted. The 4 books conceal the full variety of present concerns in laptop imaginative and prescient. The papers are equipped in topical sections on attractiveness, stereo, humans and face attractiveness, item monitoring, matching, studying and lines, MRFs, segmentation, computational images and energetic reconstruction.

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Extra info for Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part III

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Xp(sp −1)×cp +tp , . . , xpnp T , (1) where xp(sp −1)×cp +tp represents the pixel located at the coordinate position (sp , tp ) in the p-th image space, sp ∈ {1, · · · , rp }, and tp ∈ {1, · · · , cp }. In this paper, we define q = 3 − p, meaning that q = 1 when p = 2 and q = 2 when p = 1, and let i = (sp − 1) × cp + tp . For each pixel xpi , we want to find a matching point located at coordinate position (sq , tq ) in the q-th (continuous) image space, where sq , tq ∈ R. Hence, we can use a label vector to represent the position offset from a point in the second image to the corresponding point in the first image: T T yip = (vip , hpi ) = s1 , t1 − s2 , t2 ∈ R2 .

This approach is well-matched to the similarity obtained by SIFT flow (described below) as it uses the same basic features (SIFT descriptors) and spatial information is loosely represented (by means of the spatial histograms). 2 The Flow Algorithm As shown in Fig. 1, images of distinct scenes can be drastically different in both RGB values and their gradients. In addition, the magnitude of pixel displacements between potentially corresponding objects or scene parts can be much larger than typical magnitudes of motion fields for temporal sequences.

Springer, Heidelberg (2004) 8. : A search engine for 3d models. In: ACM TOG, pp. 83–105 (2003) 9. : Shape distributions. In: ACM TOG, vol. 21, pp. 807–832 (2002) 10. : Expression-invariant representations of faces. IEEE Trans. Image Processing 16(1), 188–197 (2007) 11. : Correspondence labelling for wide-timeframe free-form surface matching. In: ICCV (2007) 12. : Fusion of summation invariants in 3d human face recognition. In: CVPR (2006) 13. : A fast 3d correspondence method for statistical shape modeling.

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