Get Computer Vision – ECCV 2008: 10th European Conference on PDF

By Eduard Vazquez, Joost van de Weijer, Ramon Baldrich (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)

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

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

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Read Online or Download Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV PDF

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

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Representation and Detection of Deformable Shapes. PAMI 27(2) (2005) 13. : Interactive Graph Cut Based Segmentation with Shape Priors. In: CVPR 2005 (2005) 14. : Real-Time Object Detection for ”Smart” Vehicles. In: ICCV 1999 (1999) 15. : A graphical model framework for coupling MRFs and deformable models. In: CVPR 2004 (2004) 16. : A Segmentation Algorithm for Contrast-Enhanced Images. ICCV 2003 (2003) 17. : Effciently Solving Dynamic Markov Random Fields Using Graph Cuts. In: ICCV 2005 (2005) 18.

Our algorithm proceeds to extract the “best” possible image segment containing that point. t. S contains the user-marked point, and Seg maximizes the segmentation score of (7). Fig. 6 shows how different user-selected points-of-interest extract different objects of interest from the image (inducing different figure-ground segmentations Seg). A figure-ground segmentation can be described by assigning a label li to every pixel i in the image, where li = 1 ∀i ∈ S, and li = −1 ∀i ∈ S. We can rewrite Score (Seg) of (7) in terms of these labels: li · PES (i|S) − PES i|S + Score (Seg) = i∈I 1 2 |li − lj |·log P r (Edgei,j ) (i,j)∈N (8) where N is the set of all pairs of neighboring pixels.

Fig. 4 shows quantitatively that Score (Seg) − i,j i∈S, j∈S peaks at proper segment boundaries, and decreases when ∂S deviates from it. 36 S. Bagon, O. Boiman, and M. Irani (a) (b) Fig. 4. Score (Seg) as a function of deviations in boundary position ∂S: (a) shows the segmentation score as a function of the boundary position. It obtains a maximum value at the edge between the two textures. (b) The segmentation score as a function of the deviation from the recovered segment boundary for various segment types (deviations were generated by shrinking and expanding the segment boundary).

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