Computer Vision – ECCV 2008: 10th European Conference on by Jan J. Koenderink (auth.), David Forsyth, Philip Torr,

By Jan J. Koenderink (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)

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

The 243 revised papers provided have been conscientiously reviewed and chosen from a complete of 871 papers submitted. The 4 books conceal the complete diversity of present concerns in computing device imaginative and prescient. The papers are prepared in topical sections on attractiveness, stereo, humans and face attractiveness, item monitoring, matching, studying and contours, MRFs, segmentation, computational images and lively reconstruction.

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Additional resources for Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part I

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K. S. Chung 368 Scene Segmentation for Behaviour Correlation . . . . . . . . . . . . Jian Li, Shaogang Gong, and Tao Xiang 383 Robust Visual Tracking Based on an Effective Appearance Model . . . Xi Li, Weiming Hu, Zhongfei Zhang, and Xiaoqin Zhang 396 Key Object Driven Multi-category Object Recognition, Localization and Tracking Using Spatio-temporal Context . . . . . . . . . . . . Yuan Li and Ram Nevatia 409 A Pose-Invariant Descriptor for Human Detection and Segmentation .

In the case of continuous outputs). We note, however, that not all constraints will be active at any time, which can be seen by the equivalence between Equation (4) and ξi ≥ max Δ(yi , y) − ( w, φ(xi , yi ) − w, φ(xi , y) ) , y∈Y\yi ∀i (6) which indicates that the αiy in Equation (5) will be sparse. At training time, we can use constraint generation to solve the optimization in Equations (2)–(4). Estimates of w are trained using fixed subsets of constraints, and new constraints are added by finding the y that maximize the right hand side of Equation (6).

Algorithm 1. Branch-and-Bound Optimization Procedure Require: image I ∈ Rn×m ˆ Require: quality bounding function h Ensure: y = argmaxR⊂I f (R) initialize P as empty priority queue initialize Y˜ = [0, n] × [0, m] × [0, n] × [0, m] indicating the top, left, bottom, and right of the box could fall anywhere in I repeat split Y˜ → Y˜1 ∪˙ Y˜2 by splitting the range of one of the sides into two ˆ Y˜1 ), Y˜1 ) and ( ˆ push ( h( h(Y˜2 ), Y˜2 ) into P ˜ retrieve top state, Y, from P until Y˜ consists of only one rectangle, y We note that Equation (11) is simply a linear combination of kernel evaluations between xi|y and the support vectors, and therefore is in exactly the form that was solved for in [7].

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