By Jianru Xue, Nanning Zheng (auth.), Nanning Zheng, Xiaoyi Jiang, Xuguang Lan (eds.)

ISBN-10: 354037597X

ISBN-13: 9783540375975

This booklet constitutes the refereed complaints of the overseas Workshop on clever Computing in development Analysis/Synthesis, IWICPAS 2006, held in Xi'an, China in August 2006 as a satellite tv for pc workshop of the 18th overseas convention on development reputation, ICPR 2006.

The volumes current jointly a complete of fifty one revised complete papers and 128 revised posters papers chosen from approximately 264 submissions. The papers are prepared in topical sections on item detection, monitoring and popularity, trend illustration and modeling, visible trend modeling, photograph processing, compression and coding and texture analysis/synthesis.

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Extra resources for Advances in Machine Vision, Image Processing, and Pattern Analysis: International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006 Xi’an, China, August 26-27, 2006 Proceedings

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Two different classifiers on a database of 1662 sequences divided into 8 actions and carried out by 7 people. The classifier association is achieved by featuring a “relevancy matrix” that identifies cases where a classifier has to be preferred over the other. 2% is obtained on the database. An extension of the number of actors in the database is envisioned to be more robust to the silhouette of the person and improve classification results. Furthermore, increasing the number of examples per class may lead to a finer modeling of each class.

The main contribution of this paper can be concluded as follows: 1. Online Bayesian learning of the image observation likelihood of the tracked object with RVM classification. 2. Temporal fusion of the selected sparse vectors into an adaptive representation of the tracked object for effective and efficient tracking. 3. Embedding trained RVM classifier into particle filtering. 4. Robust estimation of the target state with mean-shift initialized EM algorithm. Beacuse of its generality, the proposed framework of online Bayesian learning could be extended to track different objects with various features.

This ensures a -tip-wedge-tip- sequence. The next step is to remove those g-edges that cannot possibly make a valid combination. The criteria are determined from the properties above. Using the first property that adjacent curves have to have opposite orientations, g-edges with orientation difference less than π2 are removed. The second property says that a g-edge must connect curves back to back, where the back is the two endpoints, not the center of the curve. To enforce the third property that there is a line token connecting the two curves, a rectangular region centered on the gedge and 7 pixels wide is searched for a line parallel to the g-edge.

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Advances in Machine Vision, Image Processing, and Pattern Analysis: International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006 Xi’an, China, August 26-27, 2006 Proceedings by Jianru Xue, Nanning Zheng (auth.), Nanning Zheng, Xiaoyi Jiang, Xuguang Lan (eds.)


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