By Yufei Han, Zhenan Sun, Fei Wang, Tieniu Tan (auth.), Yasushi Yagi, Sing Bing Kang, In So Kweon, Hongbin Zha (eds.)
The 2 quantity set LNCS 4843 and LNCS 4844 constitutes the refereed lawsuits of the eighth Asian convention on machine 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 conscientiously reviewed and seleceted from 551 submissions. The papers of this quantity are equipped in topical sections on form and texture, becoming, calbration, detection, snapshot and video processing, functions, face and gesture, monitoring, digicam 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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Extra info for Computer Vision – ACCV 2007: 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, Proceedings, Part II
Evaluate the given hand structure pair H1 and H2 . 4. The one with the best evaluation is selected as the ﬁnal answer. Fig. 3. 1 Evaluation Function After each scan of the tree, we are left with a pair of H1 and H2 . The evaluation of this pair consists of a few criteria. 1. Each of H1 and H2 must be connected. This comes from the natural requirement that each hand is a connected entity. 2. Distance of any part of H from the palm must be within a certain limit. This discourages to have a ﬁnger that is longer than a certain limit.
It turns out, however, that hand tracking using color is a non-trivial task except for well-controlled environments, as various lighting changes pose challenging conditions [3,10,15,17]. Making use of special equipment such as data gloves is one solution to overcome this diﬃculty. When the hand is given in a magniﬁed view, hand shape analysis becomes a feasible problem , although body and arm posture information might be lost. Successful results are reported by using multiple cameras for extracting 3D information [9,13].
However, vein image quality varies dramatically across the population and in case of blood vessel constriction resulting from extremely cold weather. Several advantages can be obtained by fusing the two spectral hand images. First of all, a more user-friendly system can be developed by alternatively combining the two traits or choosing the appropriate one for recognition according to corresponding imaging quality; secondly, forgery is much more diﬃcult for such an intelligent system and hence the system is more secure; and ﬁnally, the recognition performance might be boosted.