By Ali Movaghar, Mansour Jamzad, Hossein Asadi

ISBN-10: 3319108484

ISBN-13: 9783319108483

ISBN-10: 3319108492

ISBN-13: 9783319108490

This booklet constitutes the refereed court cases of the foreign Symposium, on man made 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 akin to snapshot processing, laptop imaginative and prescient, scientific photograph processing, sign processing, speech processing, traditional language processing, structures and AI purposes, robotics.

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

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Electron. 14(1), 19–26 (1976). 294460 10. : Classifying segmented hyperspectral data from a heterogeneous urban environment using support vector machines. J. Appl. Remote Sens. 1(1), 013543 (2007). 2813466 11. : A comparative study of spatial approaches for urban mapping using hyperspectral rosis images over Pavia city, northern Italy. Int. J. Remote Sens. 30(12), 3205–3221 (2009). 1080/01431160802559046 12. : Combining support vector machines and Markov random fields in an integrated framework for contextual image classification.

Springer International Publishing Switzerland 2014 A. Movaghar et al. ): AISP 2013, CCIS 427, pp. 41–50, 2014. 1007/978-3-319-10849-0_5 42 M. Borhani and H. Ghassemian Therefore, the including of information about spatial structures into classifier should improve the classification. The question is how to define these structures automatically? This information is not directly accessible from camera recordings as the spectral information. The second question is how to combine spectral and spatial information?

Then, with using a consistency verification (CV) stage increases the quality of the output image. Tang [13] by considering the issue of complexity reduction proposed two image fusion techniques DCT + average and other, based on a contrast measure defined in the DCT domain is presented (DCT + contrast). Tang has implemented his proposed methods on 8 × 8 DCT blocks defined. DCT + Average obtains the fused image by taking the average of the DCT coefficients of the input images. This method leads to some undesirable side effects including blurring.

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Artificial Intelligence and Signal Processing: International Symposium, AISP 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers by Ali Movaghar, Mansour Jamzad, Hossein Asadi


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