By Qiong Liu, You Yang, Xu Wang, Liujuan Cao (auth.), Shipeng Li, Abdulmotaleb El Saddik, Meng Wang, Tao Mei, Nicu Sebe, Shuicheng Yan, Richang Hong, Cathal Gurrin (eds.)
The two-volume set LNCS 7732 and 7733 constitutes the completely refereed court cases of the nineteenth overseas convention on Multimedia Modeling, MMM 2012, held in Huangshan, China, in January 2013.
The 30 revised normal papers, forty six targeted consultation papers, 20 poster consultation papers, and 15 demo consultation papers, and six video browser showdown have been rigorously reviewed and chosen from numeroues submissions. the 2 volumes include papers provided within the topical sections on multimedia annotation I and II, interactive and cellular multimedia, class, attractiveness and monitoring I and II, score in seek, multimedia illustration, multimedia structures, poster papers, exact consultation papers, demo consultation papers, and video browser showdown.
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Extra resources for Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part II
Therefore, text recognition has the widely applied prospect, and it is critical in content-based image retrieval, assistive navigation, scene understanding and geography annotation, etc. Although, the optical character recognition (OCR) has been successful in the application, it is suitable to deal with the scanned document in which the text is not interfered by the background and is easy to be distinguished from the background. Thus, text recognition in the wild is still a challenging problem.
It is updated when bt , yt are got as  shows. Assuming Gt is the concatenation of second measurement at time t, it is also modeled as a GMM variable. The deﬁnition and corresponding OAM are similar to above. Then, the two measurements are fused by p (yt /bt )·p (Gt /bt ), which is set as particle weight in following section. Motion Filtering by Particle Filter. Local optimization is added to particle ﬁlter for reducing the search blindness and computational eﬀort: Mahalanobis distance between yt and At is minimized by gradient descent method.
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