By Miguel Lourenço, João Pedro Barreto (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, 2nd and 3D form, 3D reconstruction, visible popularity and category, visible good points and snapshot matching, visible tracking: motion and actions, versions, optimisation, studying, visible monitoring and snapshot registration, photometry: lights and color, and snapshot segmentation.
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Extra info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV
1. Example of our tracking results in tiger1 and liquor seqs oriented gradients (HOG) is robust to pose variation when the object’s shape is consistent ; the Haar-like feature is robust to occlusion since it is part-based ; and the intensity is a good enough feature descriptor when there is small amount of pose variation and noise, because it contains redundant visual information . In our method, each tracker is implemented with a different feature based on a particle filter. Our objective is then to integrate these multiple trackers and features to achieve robust visual tracking in dynamic environment changes.
2 Problem Statement This section describes the divergence-free model, that represents motion on an image sequence. Let us denote Ω × [0, tN ] the bounded space-time domain on which images, vorticity and motion ﬁelds are deﬁned. 1 I. Herlin et al. Divergence-Free Model Vorticity characterises a rotational motion while divergence characterises sinks T and sources in a ﬂow. 2D motion w = u v is described by its vorticity, ∂v ∂u − , under the hypothesis of null divergence . ξ is chosen as the ﬁrst ξ= ∂x ∂y component of the state vector X of the model.
A Scalar Function Formulation for Optical Flow. -O. ) ECCV 1994. LNCS, vol. 801, pp. 125–131. Springer, Heidelberg (1994) 7. : Physically based ﬂuid ﬂow recovery from image sequences. In: CVPR, pp. 969–975 (1997) 8. : Optical ﬂow computation using extended constraints. Trans. on Image Processing 5, 720–739 (1996) 9. : The motion constraint equation for optical ﬂow. In: ICPR (1984) 10. : Sea surface velocities from Sea Surface Temperature image sequences. Journal of Geophysical Research 105, 19499– 19514 (2000) 11.
Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV by Miguel Lourenço, João Pedro Barreto (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)