Computer Vision - ECCV 2008: 10th European Conference on by David Forsyth, Philip Torr, Andrew Zisserman

By David Forsyth, Philip Torr, Andrew Zisserman

The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed court cases of the tenth ecu 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 disguise the whole diversity of present concerns in laptop imaginative and prescient. The papers are prepared in topical sections on reputation, stereo, humans and face acceptance, item monitoring, matching, studying and contours, MRFs, segmentation, computational images and lively reconstruction.

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

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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).

Tasks, ranging from segmentation of a single input image, through simultaneous segmentation of a pair of images (“Cosegmentation” [12]) or multiple images. The large diversity of image segment types has increased the urge to devise a unified segmentation approach. Tu et al. [13] provided such a unified probabilistic framework, which enables to “plug-in” a wide variety of parametric models capturing different segment types. ) requires its own explicit parametric model. Moreover, adding a new parametric model to this framework requires a significant and careful algorithm re-design.

Synergism in low level vision. International Conference on Pattern Recognition 4, 150–155 (2002) 24. : A comparison of image segmentation algorithms. Technical Report CMU-RI-TR-05-40, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (September 2005) 25. : New benchmark for image segmentation evaluation. Journal of Electronic Imaging 16, 033011 (2007) 26. : Efficient graph-based image segmentation. Intl. Journal of Computer Vision 59(2) (2004) 14 E. Vazquez, J. van de Weijer, and R.

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