Read e-book online Advances in Machine Vision, Image Processing, and Pattern PDF

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

This publication constitutes the refereed complaints of the foreign 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 foreign convention on development acceptance, 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, development illustration and modeling, visible development modeling, photograph processing, compression and coding and texture analysis/synthesis.

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Signal Processing 2 (2002) 174-188 18. : Quasi-Random Sampling for Condensation. In: Proceedings of ECCV. (2000) 19. : Mean Shift: A Robust Approach Toward Feature Space Analysis. PAMI 5 (2002) 603-619 20. : An EM-like Algorithm for Color-Histogram-Based Object Tracking. In: Proceedings of CVPR. (2004) 21. : Distinctive Image Features from Scale-Invariant Keypoints. IJCV 2 (2004) 91-110 22. : A Performance Evaluation of Local Descriptors. PAMI 10 (2005) 1615-1630 23. : Robust Real-Time Face Detection.

At any time t > 0, denote the feature descriptor of the tracked object region by ut . If the image observation likelihood satisfies 0 < b ≤ p(It |Xt ) ≤ a < 1, (20) then add current region descriptor ut to the object model U by incremental augmentation U = {U, ut}. If the number of elements in U is larger than Npos , the oldest elements |U | − Npos will be removed to fix the model size to be |U | = Npos . The parameter a in (20) is used to avoid redundancy resulting from too much similar region descriptors, and parameter b to resist incorrect addition of descriptors of the partially occluded regions to the object model.

2. Tracking Human Face. The frames 1, 209,365, 695, 729, 935, 956, 975, 1096 and 1145 are shown. illumination variation and continuous jitter of on-vehicle camera. In Fig. 4, the swift runner within the movie clip from Forrest Gump is tracked. The challenges include drastic variations in pose, viewpoint and scale. In addition, a portion of background pixels compassed within the rectangle boundary gives rise to appearance changes. Fig. 3. Tracking car over on-vehicle sequence. The frames 1, 224, 388, 444, 455, 508, 711, 733, 1350 and 1520 are shown.

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