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