Artificial Intelligence and Signal Processing: International by Ali Movaghar, Mansour Jamzad, Hossein Asadi

By Ali Movaghar, Mansour Jamzad, Hossein Asadi

This booklet constitutes the refereed court cases of the overseas Symposium, on synthetic Intelligence and sign Processing, AISP 2013, held in Tehran, Iran, in December 2013. The 35 complete papers provided have been rigorously reviewed and chosen from 106 submissions. they're equipped in topical sections equivalent to photo processing, computing device imaginative and prescient, scientific photograph processing, sign processing, speech processing, typical language processing, platforms and AI functions, robotics.

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Extra info for Artificial Intelligence and Signal Processing: International Symposium, AISP 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers

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The purpose is to find a feature that makes a key distinguishable so that the ownership can be protected. As described in above the new learning based contour detection method has good results in images that are taken from different point of views. The purpose is to find distinct features from these images so that they could be watermarked without having problem in the ownership of the images. The feature that we used should be good enough to have different results in images that are taken from different point of view in the same scene.

Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 113(1), S110–S122 (2009). 028 18. : Kernel grouped multivariate discriminant analysis for hyperspectral image classification. ir Abstract. Segmentation in Synthetic Aperture Radar (SAR) images has become very important in recent years because of its wide range of applications. There are lots of methods introduced for this purpose in literature. In this paper we used contourlet as a new method for feature extraction.

For 2 levels of decomposition, H1, H2 represent horizontal subbands, V1, V2 represent vertical subbands, D1, D2 represent diagonal subbands, and A2 represent approximate subband, the fusion algorithm is performed using calculates variance of these subbands coefficients in four dimensions and two scales. Here, considered the variance values of the blocks from source images as activity level measures that computed using Eq. (1).  1 X I ðPÞÞ2 ACI ðPÞ ¼ ðXI ðPÞ À X NM  ð1Þ For simplicity the processing of just two source images, If more than two source images are observed, they are combined one by one by iteratively applying the method, so we consider two source images F and G, and the fused image Z.

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