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 eu convention on machine imaginative and prescient, ECCV 2008, held in Marseille, France, in October 2008.
The 243 revised papers provided have been rigorously reviewed and chosen from a complete of 871 papers submitted. The 4 books conceal the complete variety of present concerns in laptop imaginative and prescient. The papers are geared up in topical sections on acceptance, stereo, humans and face popularity, item monitoring, matching, studying and lines, MRFs, segmentation, computational images and energetic reconstruction.
Read 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
Consequently, (x , ω ) is a global minimum of (1) and the search terminates without traversing the whole tree. In our experiments, the number of the traversed nodes was typically very small (two-three orders of magnitude smaller then the size of the full tree). Therefore, the algorithm performed global optimization much faster than exhaustive search over Ω. In order to further accelerate the search, we exploit the coherency between the mincut problems solved at diﬀerent nodes. Indeed, the maximum ﬂow as well as auxiliary structures such as shortest path trees computed for one graph may be “reused” in order to accelerate the computation of the minimal st-cut on another similar graph [3,17].
Image Segmentation by Branch-and-Mincut. Microsoft Technical Report MSR-TR-2008-100 (July 2008) 24. : Statistical Shape Inﬂuence in Geodesic Active Contours. In: CVPR 2000 (2000) 25. : ”GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 23(3) (2004) 26. : Globally Optimal Image Segmentation with an Elastic Shape Prior. In: ICCV 2007 (2007) 27. : Boundary Finding with Correspondence Using Statistical Shape Models. In: CVPR 1998 (1998) What Is a Good Image Segment?
Partially symmetric object as in Fig. b). 4 Figure-Ground Segmentation Algorithm In this section we outline our ﬁgure-ground segmentation algorithm, which optimizes Score (Seg) of (7). The goal of ﬁgure-ground segmentation is to extract an object of interest (the “foreground”) from the remaining parts of the image (the “background”). In general, when the image contains multiple objects, a user input is required to specify the “foreground” object of interest. 38 S. Bagon, O. Boiman, and M. Irani Input image Our results Init+ recovered S Results of GrabCut  Init bounding box recovered S Fig.