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 court cases of the twelfth Asian convention on computing device 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 reputation; 3D imaginative and prescient; low-level imaginative and prescient and contours; segmentation; face and gesture, monitoring; stereo, physics, video and occasions; and poster classes 1-3.

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In: Proceedings of the International Conference on Computer Vision (2013) 5. : Modeling the temporal extent of actions. , Paragios, N. ) ECCV 2010. LNCS, vol. 6311, pp. 536–548. Springer, Heidelberg (2010) 6. : Automatic annotation of human actions in video. In: Proceedings of the International Conference on Computer Vision (2009) 7. : Learning sign language by watching TV (using weakly aligned subtitles). In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2009) 8.

ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010) 54. : Space-variant descriptor sampling for action recognition based on saliency and eye movements. , Schmid, C. ) ECCV 2012. Lecture Notes in Computer Science, vol. 7578, pp. 84–97. Springer, Heidelberg (2012) 55. : Exploring stip-based models for recognizing human interactions in tv videos. PRL 34, 1819–1828 (2013) 56. : Trajectory-based modeling of human actions with motion reference points. , Schmid, C. ) ECCV 2012. LNCS, vol.

In: Proceedings of the International Conference on Machine Learning (2006) 36. : On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18, 183–190 (1988) 37. : Induced ordered weighted averaging operators. IEEE Trans. Syst. Man Cybern. 29, 141–150 (1999) 38. : Multiple instance real boosting with aggregation functions. In: Proceedings of the International Conference on Pattern Recognition (2012) 39. : Convex multiple-instance learning by estimating likelihood ratio.

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