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

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 offered have been rigorously reviewed and chosen from 106 submissions. they're prepared in topical sections resembling snapshot processing, laptop imaginative and prescient, clinical snapshot processing, sign processing, speech processing, common language processing, structures and AI purposes, robotics.

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Read or Download Artificial Intelligence and Signal Processing: International Symposium, AISP 2013, Tehran, Iran, December 25-26, 2013, Revised Selected Papers PDF

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

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In other methods, DCT + contrast, activity level is based on a contrast measure. The DCT block of the output image made up with highest contrast for AC coefficient and DC coefficient by averaging of DC coefficient produce. This algorithm is also complex in calculating the contrast measure for each coefficient and suffers from some side effects including blocking artifacts. In order to reduce the complication and enhance the quality of the output image, an image fusion technique in wavelet domain is proposed in this paper.

Survey of appearance-based methods for object recognition. D. Thesis, Graz University of Technology (2008) 4. : Optimal local basis: a reinforcement learning approach for face recognition. Int. J. Comput. Vis. 81(2), 191–204 (2009) 5. : Face detection and recognition of natural human emotion using markov random fields. Pers. Ubiquit. Comput. 13(1), 95–101 (2009) 6. : FACES - a database of facial expressions in young, middle-aged, and older women and men: development and validation. Behav. Res.

The GSA steps are represented in Fig. 2. An optimal detector equipped with this algorithm can be used to solve optimization problem where the solution can be defined as a position in the problem space. The system space is a multidimensional coordinate system defining the problem space and search agents represent a set of objects each of which is one of the problem solutions. The system is supposed to be a collection of m masses. The position of each agent (mass) which is a candidate solution for the problem is defined as follows: À Á Xi ¼ x1i ; .

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