Computer Vision – ECCV 2012: 12th European Conference on by Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei (auth.),

By Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed lawsuits of the twelfth eu convention on machine imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers awarded have been conscientiously reviewed and chosen from 1437 submissions. The papers are equipped in topical sections on geometry, 2nd and 3D shapes, 3D reconstruction, visible acceptance and category, visible positive factors and picture matching, visible tracking: motion and actions, versions, optimisation, studying, visible monitoring and picture registration, photometry: lighting fixtures and color, and photograph segmentation.

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The ordered spectral coordinates provides our geometric component in our new direct feature matching. We now briefly review how diffeomorphism can be achieved for image registration. 3 Diffeomorphic Registration The minimization of Eq. (2) does not guarantee a one-to-one mapping between points (only closest points are assigned and undefined correspondences are possible). Such Spectral Demons – Image Registration via Global Spectral Correspondence 35 Algorithm 2. Exponential φ = exp(v) Algorithm 3. The Log-Demons Framework Input: Velocity field v.

A recent work on natural image statistics [20] shows that the best match of a patch is most probably located near itself. We verify this by setting τ = 0 (thus a patch can match any other patch rather than itself). We can see that the offsets statistics have a single dominant peak around (0, 0) (Fig. 2(d)). Although the offsets distribution is even sparser (see Fig. 2(a)), the zero offset is insignificant for inferring the structures in the hole. 2 Offsets Statistics for Image Completion We further observe that the dominant offsets (with the non-nearby constraint) are informative for filling the hole under at least three situations: (i) linear structures, (ii) regular/random textures, and (iii) repeated objects.

Weakly supervised object recognition and localization with invariant high order features. In: BMVC (2010) 7. : Geometric p -norm feature pooling for image classification. In: CVPR (2011) 8. : Combining efficient object localization and image classification. In: ICCV (2009) 9. : Contextualizing object detection and classification. In: CVPR (2011) 10. : Locality-constrained Linear Coding for image classification. In: CVPR (2010) 11. : Image Classification Using Super-Vector Coding of Local Image Descriptors.

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