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

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 complaints of the ninth overseas discussion board on electronic television and instant Multimedia verbal exchange, IFTC 2012, Shanghai, China, November. The sixty nine revised complete papers provided have been rigorously reviewed and chosen from a variety of submissions. The papers are equipped in topical sections on snapshot processing and development attractiveness; photo and video research; snapshot caliber evaluation; textual content photograph and speech processing; content material retrieval and defense; resource coding; multimedia conversation; new advances in broadband multimedia; human machine 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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5) Document in several languages, including Greek, Arabic, Hindi, Nepalese, Bulgarian, Thai, Vietnamese and Hebrew. Each category has 100 document images and there are 500 test images in all. 2 Results and Analysis We compare the results of our present work with that of recently published papers [6, 8]. Both PCP [6] and PPA [8] are representations of the state-of-the-art methods, and they have outperformed the other techniques in the literatures. A detailed comparison result of three techniques is shown in Table 1 where we set C=4.

Then, we slide the window with the step of C/2 on the image and calculate the corresponding feature value within the sliding window. These two values are denoted by H(i) and V(j) respectively, where i, j is the column and row index of the sub image. In the end, these values are summed up as the final value of the Haar-like feature for the whole image using formulation (1)-(2), FeatureH FeatureV ∑|H i | , i 0, C⁄2 , C ∑|V j | , j 0, C⁄2 , C . (1) . (2) Fig. 1. Calculation of two-rectangle feature`s value.

Wavelet transform can well analyze regional signals, and achieve good result in separating signal and noise by taking advantage of their differences shown in both time and frequency domain. Yet wavelet decomposition focuses on approximate coefficient vector, which leads to loss of original features in time domain during the de-noising preprocess as it continues decomposing in lower frequency direction. Wavelet packet transform is proposed in order to overcome wavelet decomposition’s low frequency resolution ratio in high-frequency section and weak time resolution ratio in low-frequency section.

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