Download Advances on Digital Television and Wireless Multimedia by Chao Jiang, Ze-xun Geng, Xiao-feng Wei, Chen Shen (auth.), PDF

By Chao Jiang, Ze-xun Geng, Xiao-feng Wei, Chen Shen (auth.), Wenjun Zhang, Xiaokang Yang, Zhixiang Xu, Ping An, Qizhen Liu, Yue Lu (eds.)

This publication constitutes the refereed court cases of the ninth overseas discussion board on electronic television and instant Multimedia communique, IFTC 2012, Shanghai, China, November. The sixty nine revised complete papers awarded have been rigorously reviewed and chosen from quite a few submissions. The papers are geared up in topical sections on picture processing and development acceptance; snapshot and video research; snapshot caliber evaluate; textual content snapshot and speech processing; content material retrieval and safety; resource coding; multimedia verbal exchange; new advances in broadband multimedia; human desktop interface; 3D video.

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Additional info for Advances on Digital Television and Wireless Multimedia Communications: 9th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2012, Shanghai, China, November 9-10, 2012. Proceedings

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Therefore, the projection results can then be used as the color texture feature for image segmentation. This procedure includes two steps: 1. Feature Extraction. We train the classifier on a small amount of samples from the image. By applying the QSVD algorithm to the matrix of training data Tq, whose size is W2×MN. Each column of eigenvectors U represents a basis function of the transformation. Supposing that the first K columns of U can represent the dominant structures of the whole image, we can reduce the training data Tq to TqK, where TqK = UqK ·Tq.

J /2 j /2 W2n+1( x) = 2  gkWn (2 x − k ) k  (4) in which W0 (x) is scale function or primary function, W1 (x) is the respective j /2 j /2 x − k ) is denoted as wavelet packet wavelet function, and 2  hk Wn (2 k function. As to h k and g k , they meet h = δ kl  h n∈Ζ n−2 k n−2l . k  hn = 2, g k = ( −1) h1−k n∈Ζ (5) After deducing from format (4), we get wavelet packet’s double-scale equation set: W ( x ) = 2  h Wn (2 x − k )  2n k k .  W ( x ) = 2 g W (2 x − k )   2n+1 n  k k 3 (6) De-noising Model and Principle in Wavelet Packet The filtering process of wavelet packet is generally completed in three steps.

E. set a = a 0j , a 0 > 0 , j ∈ Z , and its respective wavelet function : −j −j a02 ψ [ a0 (t − τ )], j = 0,1, 2 … . in which τ = ka jτ . Usually we uniformly discrete 0 0 axis, and τ (3) τ so as to cover the whole time j meets Nyquist sampling theorem. When a = 2 , the responsive sampling interval is 2 τ0 along τ axis. And when a0 = 2 , if j increases by 1, a doubles but its respective frequency reduces to half. In this case, the sampling rate halves without losing the information we need. ; k ∈Z .

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