By Tian Fang, Long Quan (auth.), Kostas Daniilidis, Petros Maragos, Nikos Paragios (eds.)
The 2010 variation of the eu convention on desktop imaginative and prescient was once held in Heraklion, Crete. the decision for papers attracted an absolute list of 1,174 submissions. We describe the following the choice of the authorized papers: Thirty-eight region chairs have been chosen coming from Europe (18), united states and Canada (16), and Asia (4). Their choice used to be according to the next standards: (1) Researchers who had served a minimum of twice as quarter Chairs in the prior years at significant imaginative and prescient meetings have been excluded; (2) Researchers who served as quarter Chairs on the 2010 computing device imaginative and prescient and trend attractiveness have been additionally excluded (exception: ECCV 2012 software Chairs); (3) Minimization of overlap brought via region Chairs being former scholar and advisors; (4) 20% of the realm Chairs had by no means served ahead of in an incredible convention; (5) the realm Chair choice procedure made all attainable efforts to accomplish an affordable geographic distribution among nations, thematic parts and tendencies in laptop imaginative and prescient. every one quarter Chair used to be assigned by means of this system Chairs among 28–32 papers. in keeping with paper content material, the realm Chair steered as much as seven strength reviewers in line with paper. Such task was once made utilizing all reviewers within the database together with the conflicting ones. this system Chairs manually entered the lacking clash domain names of roughly three hundred reviewers. in response to the advice of the realm Chairs, 3 reviewers have been chosen in keeping with paper (with a minimum of one being of the head 3 suggestions), with 99.
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Additional info for Computer Vision – ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part II
We use F2 feature to measure this dissimilarity: wj,k = 1 M M dis(F2 (e(j,k) ), F2 (eti,i+1 )) (8) t=1 Notice that the way for computing F2 feature on a edge map is diﬀerent from the case for training template, since we do not know the adjacent junctions on a edge map. For junctions Jj and Jk , we can obtain their related paths P (Jj ) and P (Jk ) (as shown Fig. 2 ﬁrstly. , 10) at equal space (see Fig. 7 (b)); For each pt , we compute its shape contexts feature on 50 equally sample points (see Fig.
Path altogether and ei−1,i and ei,i+1 may not have 5 points each since they do necessarily have the same length. For each path point pt , we compute its feature h(pt ) based on 50 densely sampled points on path P (Ji ) at equal space. Fig. c) gives an illustration. The parameter setting for computing the histogram of shape context is the same as that in : 5 distance scales and 12 angle scales. Thus, each h(pt ) can be viewed as a feature vector of length 60. , h(p10 ) T , (1) which is of length 60 × 10 = 600.
Donoser, and H. Bischof the foreground and which are background clutter. By this labeling they reduce the clutter and focus on salient edges in their veriﬁcation step. Lu et al. use particle ﬁlters under static observation to simultaneously group and label the edge contours. They use a new shape descriptor based on angles to decide edge contour similarity. Zhu et al. use control points along the reference contour to ﬁnd possible edge contour combinations and then solve cost functions eﬃciently using linear programming.
Computer Vision – ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part II by Tian Fang, Long Quan (auth.), Kostas Daniilidis, Petros Maragos, Nikos Paragios (eds.)