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The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed complaints of the tenth eu convention on desktop imaginative and prescient, ECCV 2008, held in Marseille, France, in October 2008.
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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 IV
Branch and Bound Algorithms - Principles and Examples. Parallel Computing in Optimization (1997) 9. : Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation. IJCV 69(3) (2006) 10. : Shape Priors in Variational Image Segmentation: Convexity, Lipschitz Continuity and Globally Optimal Solutions. In: CVPR 2008 (2008) 11. : Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society 51(2) (1989) 12. : Representation and Detection of Deformable Shapes.
In particular, it should be noted that the way our framework handles shape priors is related to previous approaches like  that used tree search over shape hierarchies. However, neither of those approaches accomplish pixel-wise image segmentation. 3 Optimization Framework In this section, we discuss our global energy optimization framework for obtaining image segmentations under non-local priors1 . In the next sections, we detail how it can be used for the segmentation with non-local shape priors (Section 4) and non-local intensity/color priors (Section 5).
In terms of performance, for M = 8 the segmentation takes on average a few dozen seconds (10s and 40s for the images in Fig. 4) for 300x225 image. The proportion of the tree traversed by an active front is one to several hundred (1:963 and 1:283 for the images in Fig. 4). Image Segmentation by Branch-and-Mincut 27 Image+input GrabCut(−618) Branch&Mincut(−624) Combined(−628) Image+input GrabCut(−593) Branch&Mincut(−584) Combined(−607) Fig. 4. Being initialized with the user-provided bounding rectangle (shown in green in the ﬁrst column) as suggested in , EM-style process  converges to a local minimum (the second column).