By Tatiana Tommasi, Novi Quadrianto, Barbara Caputo, Christoph H. Lampert (auth.), Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu (eds.)
The four-volume set LNCS 7724--7727 constitutes the completely refereed post-conference lawsuits of the eleventh Asian convention on machine imaginative and prescient, ACCV 2012, held in Daejeon, Korea, in November 2012. the complete of 226 contributions offered in those volumes was once conscientiously reviewed and chosen from 869 submissions. The papers are geared up in topical sections on item detection, studying and matching; item attractiveness; characteristic, illustration, and popularity; segmentation, grouping, and class; snapshot illustration; photo and video retrieval and clinical photograph research; face and gesture research and popularity; optical movement and monitoring; movement, monitoring, and computational images; video research and motion popularity; form reconstruction and optimization; form from X and photometry; functions of laptop imaginative and prescient; low-level imaginative and prescient and purposes of machine vision.
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Additional info for Computer Vision – ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I
Jie Geng, Zhenjiang Miao, and Hai Chi 547 Features with Feelings—Incorporating User Preferences in Video Categorization . . . . . . . . . . . . . . . . . . . . . . . . . . Ramya Srinivasan and Amit K. Roy-Chowdhury 560 A Comparative Study of Encoding, Pooling and Normalization Methods for Action Recognition . . . . . . . . . . . . . . . . . . . . . . Xingxing Wang, LiMin Wang, and Yu Qiao 572 Dynamic Saliency Models and Human Attention: A Comparative Study on Videos .
Simon Hermann and Reinhard Klette Action Recognition Using Canonical Correlation Kernels . . . . . . . G. V. Jawahar 465 479 XXXVI Table of Contents – Part III A New Framework for Background Subtraction Using Multiple Cues . . SeungJong Noh and Moongu Jeon 493 Weighted Interaction Force Estimation for Abnormality Detection in Crowd Scenes . . . . . . . . . . . . . . . . . . . . . . . . . Xiaobin Zhu, Jing Liu, Jinqiao Wang, Wei Fu, and Hanqing Lu 507 Egocentric Activity Monitoring and Recovery .
In: CVPR (2011) 9. : Learning bounds for domain adaptation. In: NIPS (2008) 10. : Analysis of representations for domain adaptation. In: NIPS (2007) 11. : Domain adaptation with structural correspondence learning. In: EMNLP (2006) 12. : Frustratingly easy domain adaptation. In: ACL (2007) 13. : Adapting Visual Category Models to New Domains. , Paragios, N. ) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010) 14. : Domain adaptation for object recognition: An unsupervised approach.