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 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 awarded have been rigorously reviewed and chosen from a complete of 871 papers submitted. The 4 books disguise the complete diversity of present matters in machine imaginative and prescient. The papers are prepared in topical sections on acceptance, stereo, humans and face popularity, item monitoring, matching, studying and lines, MRFs, segmentation, computational images and lively reconstruction.
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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
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. 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.
A default image distribution). , the “savings” in the number of bits obtained by describing Q as composed from regions in Ref vs. the ‘default’ number of bits required to describe Q using H0 . The larger the regions Ri composing Q the higher the savings in description length. High savings in description length provide high statistical evidence for the similarity of Q to Ref . In order to avoid the computationally-intractable marginalization over all possible query partitions, the following approximation was derived in : DL (Q|H0 ) − DL (Q|Ref ) ≈ PES (i|Ref ) (3) i∈Q where PES (i|Ref ) is a pointwise measure (a Point-Evidence-Score) of a pixel i: PES (i|Ref ) = max R⊂Q,i∈R p (R|Ref ) 1 log |R| p (R|H0 ) (4) 1 Intuitively, given a region R, |R| log (p (R|Ref ) /p (R|H0 )) is the average savings per pixel in the region R.
Thus, the sequence of intermediate ﬂows provides a sequence of the increasing lower bounds on (1) converging to the bound (2) (flow bounds). If some upper bound on the minimum value is imposed, the process may be terminated earlier without computing the full maxﬂow/mincut. This happens when the new ﬂow bound exceeds the given upper bound. In this case it may be concluded that the value of the global minimum is greater than the imposed upper bound. 3 Branch-and-Bound Optimization Finding the global minimum of (1) is, in general, a very diﬃcult problem.
Computer Vision – ECCV 2008: 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV by Eduard Vazquez, Joost van de Weijer, Ramon Baldrich (auth.), David Forsyth, Philip Torr, Andrew Zisserman (eds.)