By Jianru Xue, Nanning Zheng (auth.), Nanning Zheng, Xiaoyi Jiang, Xuguang Lan (eds.)
This e-book constitutes the refereed complaints of the overseas Workshop on clever Computing in trend Analysis/Synthesis, IWICPAS 2006, held in Xi'an, China in August 2006 as a satellite tv for pc workshop of the 18th foreign convention on development attractiveness, 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 development modeling, picture processing, compression and coding and texture analysis/synthesis.
Read Online or Download 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 PDF
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Additional info 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
Let cln be the class returned by the nearest center classifier, cld the one returned by AANNs, and cl the true class. The cell (i,j) of the “relevancy matrix” provides the probability that the classifier returns the true result while the two classifiers respectively select classes i and j: M n (i, j ) = P(cl = cln | i, j ) . ® ¯ M d (i, j ) = P(cl = cld | i, j ) (4) When both classifiers lead to the same result, the decision is evident. Otherwise, when the returned classes are different, the “relevancy matrix” will be used to decide among the two classes, which is the true class.
For the tracking step, works are often based on multiple hypotheses analysis , other on statistics [2,3]. We chose to use color and texture combined with some hypotheses analysis to determine new and previous objects in the scene. That is, we track by appearance. Finally for the third step, we focus on tracking object relationships regardless of their identity. This will allow us analyzing speciﬁc behaviors of the detected objects such as the transportation of objects , or an illegal entry in a forbidden area .
8 32 A. Mokhber et al. Table 4. 1 6 Association of Classifiers Results presented in the two previous sections show that classifiers do not have the same behavior. It often appears that if one classifier is wrong, the other one selects the true result. Therefore, it would be of interest to identify the cases where one classifier has to be preferred over the other, especially when the outcomes of the two classifiers are different. Thus, for each classifier, a “relevancy matrix” is constructed based on the training database.