Computer Vision -- ACCV 2014: 12th Asian Conference on by Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

By Daniel Cremers, Ian Reid, Hideo Saito, Ming-Hsuan Yang

The five-volume set LNCS 9003--9007 constitutes the completely refereed post-conference complaints of the twelfth Asian convention on desktop imaginative and prescient, ACCV 2014, held in Singapore, Singapore, in November 2014.
The overall of 227 contributions awarded in those volumes was once rigorously reviewed and chosen from 814 submissions. The papers are equipped in topical sections on acceptance; 3D imaginative and prescient; low-level imaginative and prescient and lines; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster classes 1-3.

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Extra resources for Computer Vision -- ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V

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32, 1627–1645 (2010) 14. : Solving the multiple-instance problem with axis-parallel rectangles. Artif. Intell. 89, 31–71 (1997) 15. : A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems (1998) 16. : EM-DD: an improved multiple-instance learning technique. In: Advances in Neural Information Processing Systems (2002) 17. : Multiple-instance ranking: learning to rank images for image retrieval. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2008) 18.

The optimal states that maximize the posterior distribution could be then estimated by maximizing the marginals independently. 3 Edge/Activity Context Potential The activity context potential is defined on the edges of the graph, in each of Etobs , Etunobs , Etsemi−obs . This potential function models the association between any two activities occurring immediately one after the other or in close spatiotemporal succession. For any two nodes nik and njl (the corresponding labels being xik and xjl respectively) such that nik , njl ∈ Et , the inter-activity potential is given as, s ψ xik = cm , xjl = cn = fmn,1 if i = j, |l − k| = 1 s if i = j, |l − k| = 2 = fmn,2 d if i = j = fmn (2) s s d All these values fmn,1 , fmn,2 and fmn are computed from the annotated s training data.

In: Proceedings of the International Conference on Computer Vision (2007) 31. : Weakly supervised discriminative localization and classification: a joint learning process. In: Proceedings of the International Conference on Computer Vision (2009) Improving Human Action Recognition Using Score Distribution and Ranking 19 32. : Discriminative subvolume search for efficient action detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009) 33. : Joint segmentation and classification of human actions in video.

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Categories: Computer Vision Pattern Recognition