Xianzheng Song, Jian Yang, Weijian Cong (auth.), Tieniu Tan,'s Advances in Image and Graphics Technologies: Chinese PDF

By Xianzheng Song, Jian Yang, Weijian Cong (auth.), Tieniu Tan, Qiuqi Ruan, Xilin Chen, Huimin Ma, Liang Wang (eds.)

This e-book constitutes the refereed lawsuits of the chinese language convention on photograph and portraits applied sciences and purposes, IGTA 2013, held in Beijing, China, in April 2013. The forty papers and posters awarded have been rigorously reviewed and chosen from 89 submissions. The papers tackle matters resembling the iteration of latest rules, new ways, new ideas, new purposes and new assessment within the box of photo processing and graphics.

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Additional resources for Advances in Image and Graphics Technologies: Chinese Conference, IGTA 2013, Beijing, China, April 2-3, 2013. Proceedings

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The structure of ART2 neural network is similar to ART1 neural network, it also constitute by comparison layer F1, discriminating layer F2, reforming module and the gain control module, the main difference with ART1 network is the comparison layer. As shown in figure 4, the comparison layer of ART2 neural network is much more complex than the ART1 neural network. The comparison layer of ART2 network contains six sub-layers as w, x, v, u, p and q, as well as three gain modules G1, G2, G3. All of the sub-layers and the r layer of orientation sub-system contain the same number of neurons.

4(c),(d). The proposed watermark embedding algorithm has an overall consideration in the image information and the human visual perception, using EHD in MPEG-7 to analyze the image feature in order to build a suitable JND model. Extracting is an inverse process of embedding, so we make no more introductions in this article. A Digital Watermarrking Algorithm Based on EHD Image Feature Analysis 4 25 Experiments and Results In order to verify the validiity of digital image watermarking algorithm in this artiicle, we give the transparency an nd robustness test as follows, and compare with traditioonal content watermarking algo orithm based on Barni JND Model [9].

The ART2 Neural Network Based on the Adaboost Rough Classification 49 Assuming that i represent the input vector, ii is i-th component of the input vector. , Mi, ρ is the alert threshold. 1 0<ϑ<1/ sqrt (N) 0<ρ≤1 e<<1 The connection weight of the F2 layer to the p-layer zij(0) = 0 The connection weight of the p-layer to the F2 layer z’ji≤1/((1-d)*sqrt(N)) The calculation steps of ART2 neural network are as follows: 1) Set up a counter, and set the initial value of 1, the output of all the neurons are setting to 0.

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