Download Computer Vision – ECCV 2010: 11th European Conference on by Anush K. Moorthy, Pere Obrador, Nuria Oliver (auth.), Kostas PDF

By Anush K. Moorthy, Pere Obrador, Nuria Oliver (auth.), Kostas Daniilidis, Petros Maragos, Nikos Paragios (eds.)

The 2010 version of the eu convention on laptop imaginative and prescient used to be held in Heraklion, Crete. the decision for papers attracted an absolute checklist of 1,174 submissions. We describe right here the choice of the approved papers: Thirty-eight sector chairs have been chosen coming from Europe (18), united states and Canada (16), and Asia (4). Their choice was once in line with the next standards: (1) Researchers who had served a minimum of twice as region Chairs in the previous years at significant imaginative and prescient meetings have been excluded; (2) Researchers who served as sector Chairs on the 2010 computing device imaginative and prescient and development acceptance have been additionally excluded (exception: ECCV 2012 software Chairs); (3) Minimization of overlap brought by means of zone Chairs being former pupil and advisors; (4) 20% of the world Chairs had by no means served earlier than in a massive convention; (5) the world Chair choice method made all attainable efforts to accomplish a cheap geographic distribution among nations, thematic parts and developments in laptop imaginative and prescient. each one zone Chair used to be assigned by means of this system Chairs among 28–32 papers. in line with paper content material, the realm Chair suggested as much as seven strength reviewers in step 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. according to the advice of the world Chairs, 3 reviewers have been chosen according to paper (with at the least one being of the head 3 suggestions), with 99.

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Extra resources for Computer Vision – ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part V

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In such a setting the similarity to the prototype shape decides the complexity and length of the considered contours. 3 Partial Shape Matching for Object Detection In the following sections we describe our proposed approach to detect objects by computing partial similarities between edge contours in a query image and a reference template. For the sake of clarity, we will now define some terms used throughout the paper, see Figure 2 for a visual illustration. We use the term fragment to denote a part of an edge contour.

Third, it represents the connectedness of contours by using the sequence information providing a local (close to matrix diagonal) and global (far from matrix diagonal) description. And most importantly, the definition as a self-containing descriptor allows to implicitly retrieve partial matches which is a key requirement for cluttered and broken edge results. Fig. 3. Visualization of descriptors for selected contour primitives. Middle row shows descriptors from [25] and bottom row shows our descriptors.

A recent hierarchical approach by Kokkinos and Yuille [24] formulates the task as image parsing and provide fast coarse to fine matches. Lu et al. [9] developed a shape descriptor based on a 3D histogram of angles and distances for triangles connecting points sampled along the contours. They do not allow partial matching and the descriptor requires high computational costs. Donoser et al. [25] developed a descriptor which can be seen as a subset of [9], where angles between any two points and a fixed third point on a closed contour are analyzed.

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