By Xinxue Ma, Jianli Wang, Bin Wang, Tianyu Lv (auth.), Aly A. Farag, Jian Yang, Feng Jiao (eds.)
ISBN-10: 3642414060
ISBN-13: 9783642414060
ISBN-10: 3642414079
ISBN-13: 9783642414077
Proceedings of the third foreign convention on Multimedia expertise (ICMT2013) focuses on either the speculation and functions of multimedia know-how. the hot advances, new examine findings and functions within the fields of theoretical, experimental and utilized snapshot & video processing and multimedia know-how awarded on the convention are introduced jointly during this publication. it's going to function a worthy reference for scientists and engineers operating in multimedia and similar fields.
Prof. Aly A. Farag works on the collage of Louisville, united states; Prof. Jian Yang works at Tsinghua college, China; Dr. Feng Jiao works at Nanjing collage of data technology & know-how, China.
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Extra info for Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013)
Sample text
Yi and F. 1 Histogram Matching Since the GOHS is a sequence of histograms, the histogram intersection is applied as the similarity measure for histogram matching (HM). The histogram intersection is defined as: sðh1 ; h2 Þ ¼ N X minðh1 ðiÞ; h2 ðiÞÞ ð4:7Þ i¼1 where h1 and h2 are two histograms, h1 ðiÞ and h2 ðiÞ are ith bin of h1 and h2 ,respectively, and N is the number of histogram bins. This method utilizes no other information except the original GOH feature, which can test the feature description ability of the GOH.
Thus, probability of HO occurrence during the movement of MT across the heterogeneous wireless networks can be minimized. 6 Conclusion and Further Work In this paper, CU-ART-based network selection algorithm was proposed to select the best suitable network based on MT velocity, direction, utility function, and estimated residence time. Adaptive residence time threshold is also calculated for MT velocity varying from 5 to 25 m/s. It is observed that adaptive residence time threshold is high for high speed users to maintain service continuity with guaranteed QoS and minimal VHO rate.
1 Feature detection with SIFT and Harris together points on the image [8]. Harris algorithm is simple and only uses the first-order differential gray. And this algorithm also has a high stability and robustness [9], so that it can be used as a supplement in SIFT feature detection. In Fig. 1, the mulberry arrows represented the main gradient direction of SIFT feature vectors and the red points represented the Harris corner points. According to statistics, 35 pairs of feature points were extracted by SIFT while 19 pairs of corner points were added by Harris algorithm.
Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013) by Xinxue Ma, Jianli Wang, Bin Wang, Tianyu Lv (auth.), Aly A. Farag, Jian Yang, Feng Jiao (eds.)
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