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 variation of the eu convention on desktop 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 quarter chairs have been chosen coming from Europe (18), united states and Canada (16), and Asia (4). Their choice used to be in response to the next standards: (1) Researchers who had served at the least twice as zone Chairs in the prior years at significant imaginative and prescient meetings have been excluded; (2) Researchers who served as zone Chairs on the 2010 machine imaginative and prescient and trend acceptance have been additionally excluded (exception: ECCV 2012 software Chairs); (3) Minimization of overlap brought through quarter Chairs being former pupil and advisors; (4) 20% of the realm Chairs had by no means served sooner than in an immense convention; (5) the world Chair choice procedure made all attainable efforts to accomplish an inexpensive geographic distribution among nations, thematic components and tendencies in desktop imaginative and prescient. every one region Chair used to be assigned through this system Chairs among 28–32 papers. in accordance with paper content material, the realm Chair advised as much as seven strength reviewers in step with paper. Such project used to be 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 step with paper (with at the very 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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Using contour to detect and localize junctions in natural images. In: CVPR (2008) 2. : Robust real-time face detection. IJCV 57, 137–154 (2004) 3. : Discriminative random fields: a discriminative framework for contextual interaction in classification. In: ICCV (2003) 4. : A discriminatively trained, multiscale, deformable part model. In: CVPR (2008) 5. : Multiple component learning for object detection. , Zisserman, A. ) ECCV 2008, Part II. LNCS, vol. 5303, pp. 211–224. Springer, Heidelberg (2008) 6.

The videos and the subjective ratings have been made available publicly7 . We plan on increasing the number of videos in our ground truth database and conduct a larger scale user study. Future work includes exploring temporal models to characterize video aesthetics, investigating personalization techniques and shedding light on which features of our aesthetics model may be universal vs. person-dependent, and assessing the influence of audio in aesthetic ratings so as to form a complete measure of audio-visual aesthetics.

1990) 29. : The Nature of Statistical Learning Theory. Springer, Heidelberg (2000) 30. tw/~ cjlin/libsvm/ Object Recognition Using Junctions Bo Wang1 , Xiang Bai1 , Xinggang Wang1 , Wenyu Liu1 , and Zhuowen Tu2 1 Dept. edu Abstract. In this paper, we propose an object detection/recognition algorithm based on a new set of shape-driven features and morphological operators. Each object class is modeled by the corner points (junctions) on its contour. We design two types of shape-context like features between the corner points, which are efficient to compute and effective in capturing the underlying shape deformation.

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