Computer Vision – ECCV 2012: 12th European Conference on by Oren Freifeld, Michael J. Black (auth.), Andrew Fitzgibbon,

By Oren Freifeld, Michael J. Black (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed court cases of the twelfth ecu convention on desktop imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers offered have been conscientiously reviewed and chosen from 1437 submissions. The papers are equipped in topical sections on geometry, second and 3D form, 3D reconstruction, visible acceptance and category, visible good points and photograph matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and picture registration, photometry: lighting fixtures and color, and snapshot segmentation.

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

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On either side, in the same pose, we showed the PGA and PCA 12 O. J. Black Fig. 5. Perceptual task. Examples images shown in the shape perception experiment (workers did not see the “M” and “E” notations). The true mesh is always in the center. On either side, at random, is either the manifold representation (M) or the Euclidean representation (E) generated with 20 coefficients. Workers had to indicate which body looked more like the center. e. the manifold was selected as better more than 90% of the time) and red indicates “hard” examples where workers were roughly split in their decisions Fig.

We now describe gT exp−1 (GT ), the Lie algebra of GT . Naturally, it is defined using the direct product of the following three Lie algebras: gT = so(3)×gA ×gS , where 1) gS exp−1 (GS ) is R. 2) so(3) exp−1 (SO(3)) is well known to coincide with A ∈ gl(3) : A = −AT . 3) gA exp−1 (GA ) is to be described shortly. The computation of the matrix exponential (or logarithm) involves an infinite sum (unless A is diagonalizable or nilpotent). , for exp : so(3) → SO(3) and log : SO(3) → so(3), computations are given by the well known Rodrigues’ formula [25].

Experiments were performed with average descriptors as well as with all feature descriptors for the points. The results show that bidirectional matching significantly boosts the registration rate, whether or not co-occurrence based RANSAC is used. Similarly, the use of co-occurrence is also always beneficial, with or without bidirectional matching, and the advantage is more pronounced when all feature descriptors are used, as this produces more matches but also more outliers. Since co-occurrence together with bidirectional matching produced the highest performance, we use this combination for the remaining experiments.

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