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

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 desktop imaginative and prescient, ECCV 2008, held in Marseille, France, in October 2008.

The 243 revised papers offered have been rigorously reviewed and chosen from a complete of 871 papers submitted. The 4 books conceal the total diversity of present matters in computing device imaginative and prescient. The papers are prepared in topical sections on popularity, 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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The overall set Ω is then defined as {0, 1}2M , where odd bits correspond to the foreground mixture vector β f and even bits correspond to the background mixture vector β b . Vectors with all even bits and/or all odd bits equal to zero do not correspond to meaningful mixtures and are therefore assigned an infinite cost. ). Depending on the image and the value of M , the solutions found by Branchand-Mincut framework may have larger or smaller energy (8) than the solutions found by the original EM-style method [25].

Serrat, J. ) IbPRIA 2007. LNCS, vol. 4477, pp. 55–62. Springer, Heidelberg (2007) 20. : Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6), 583–598 (1991) 21. : Multiresolution analysis of ridges and valleys in greyscale images. IEEE Trans. Pattern Anal. Mach. Intell. 15(6), 635–646 (1993) 22. : Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell.

In contrast, our figure-ground segmentation algorithm requires a minimal amount of user input – a single user-marked point on the foreground segment/object of interest. 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).

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