By Chuan-Yu Chang, Shu-Han Chang, Shao-Jer Chen (auth.), Jeng-Shyang Pan, Shyi-Ming Chen, Ngoc Thanh Nguyen (eds.)

This quantity composes the complaints of the second one foreign convention on Computational Collective Intelligence––Technologies and functions (ICCCI 2010), which was once hosted through nationwide Kaohsiung college of technologies and Wroclaw college of expertise, and was once held in Kaohsiung urban on November 10-12, 2010. ICCCI 2010 used to be technically co-sponsored by means of Shenzhen Graduate college of Harbin Institute of expertise, the Tainan bankruptcy of the IEEE sign Processing Society, the Taiwan organization for net Intelligence Consortium and the Taiwanese organization for client Electronics. It aimed to collect researchers, engineers and po- cymakers to debate the similar strategies, to interchange examine rules, and to make neighbors. ICCCI 2010 excited about the next topics: • Agent concept and alertness • Cognitive Modeling of Agent platforms • Computational Collective Intelligence • computing device imaginative and prescient • Computational Intelligence • Hybrid platforms • clever snapshot Processing • info Hiding • computing device studying • Social Networks • net Intelligence and interplay round 500 papers have been submitted to ICCCI 2010 and every paper used to be reviewed by means of a minimum of referees. The referees have been from universities and business agencies. a hundred and fifty five papers have been accredited for the ultimate technical software. 4 plenary talks have been kindly provided via: Gary G. Yen (Oklahoma kingdom college, USA), on “Population regulate in Evolutionary Multi-objective Optimization Algorithm,” Chin-Chen Chang (Feng Chia collage, Taiwan), on “Applying De-clustering thought to details Hiding,” Qinyu Zhang (Harbin Institute of know-how, China), on “Cognitive Radio Networks and Its Applications,” and Lakhmi C.

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Additional info for Computational Collective Intelligence. Technologies and Applications: Second International Conference, ICCCI 2010, Kaohsiung, Taiwan, November 10-12, 2010. Proceedings, Part III

Example text

F (r , g ) = 100 % M ×N ⎧⎪ M N ⎫ ⎨ ∑ ∑ S [m , n ; r , g ] ⎬ ⎪⎩S

This is because the superposition principle works in this algorithm. Evaluations showing multiple GPM peaks require further gain-adjusted analysis. 3 Experimental Results and Discussion To demonstrate the performance of the GPM operation and CC measure, two natural images and their experimental results are reported. One image had a slight color cast, while the other had an obvious color cast. Firstly, as shown in Fig. 1, the publicly available color image ‘Barbara’ was used. Fig. 05. The presence of these grayscale pixels in Fig.

For most images, a dominant GPM peak usually exists in the diagram. Some images may have multiple peaks in the GPM diagram. Depending on the circumstance, further gain-vs-pixel analysis may be required to find out the true CC gain location. In some uncommon cases, the CC gain location may appear as a smaller GPM peak. False peaks or heightened response areas are usually created by large areas of lightly colored pixels, such as light skin color or the like. Secondary peaks may also be created by a different and separate illuminant in the scene.

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Computational Collective Intelligence. Technologies and by Chuan-Yu Chang, Shu-Han Chang, Shao-Jer Chen (auth.),
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