Download e-book for iPad: Computer Vision – ACCV 2007: 8th Asian Conference on by Yufei Han, Zhenan Sun, Fei Wang, Tieniu Tan (auth.), Yasushi

By Yufei Han, Zhenan Sun, Fei Wang, Tieniu Tan (auth.), Yasushi Yagi, Sing Bing Kang, In So Kweon, Hongbin Zha (eds.)

ISBN-10: 3540763899

ISBN-13: 9783540763895

The 2 quantity set LNCS 4843 and LNCS 4844 constitutes the refereed complaints of the eighth Asian convention on computing device imaginative and prescient, ACCV 2007, held in Tokyo, Japan, in November 2007. The forty six revised complete papers, three planary and invited talks, and one hundred thirty revised poster papers of the 2 volumes have been rigorously reviewed and seleceted from 551 submissions. The papers of this quantity are geared up in topical sections on form and texture, becoming, calbration, detection, photo and video processing, purposes, face and gesture, monitoring, digital camera networks, face/gesture/action detection and popularity, studying, movement and monitoring, retrival and seek, human pose estimation, matching, face/gesture/action detection and popularity, low point imaginative and prescient and phtometory, movement and monitoring, human detection, and segmentation.

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Additional resources for Computer Vision – ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II

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Morphological pyramid method introduces too many artifacts which contribute most to performance degradation. 6 Conclusion and Discussion In this paper, we proposed the idea of multispectral palm image fusion for biometrics. This concept extends the visual capability of camera and will improve user-friendliness, security and hopeful recognition performance of original palmprint based biometric system. Several image fusion based approaches are evaluated in the context of discriminative features. Experimental results suggest 20 Y.

T (a) Fit the regression function ft (x) by weighted least squares of yi to xi with weights wi . (b) Update F (x) ← F (x) + ft (x) È (c) Update wi ← wi e−yi ft (xi ) and renormalize. 3. Output the classifier sign[F (x)] = sign[ T t=1 ft (x)] Fig. 2. Algorithm of Gentle AdaBoost Empirical evidence suggests that Gentle AdaBoost is a more conservative algorithm that has similar performance to both the Real AdaBoost and LogitBoost algorithms, and often outperforms them both, especially when stability is a crucial issue [16].

After a connectivity graph G is formed, the remaining task is to determine parts of G that come from the right and the left hands, respectively. Fig. 2. Example of the skelton structure (left). Example of the augmented graph structure (right). Two methods are presented. The first algorithm is based on a tree search paradigm, while the second one is formulated by using constrained optimization. 3 Tree Search Framework The first algorithm for hand disambiguation uses tree search. Given an augmented graph G, we form two subgraphs H1 and H2 such that G = H1 ∪ H2 .

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Computer Vision – ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II by Yufei Han, Zhenan Sun, Fei Wang, Tieniu Tan (auth.), Yasushi Yagi, Sing Bing Kang, In So Kweon, Hongbin Zha (eds.)


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