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

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 overseas convention on Computational Collective Intelligence––Technologies and functions (ICCCI 2010), which used to be hosted by means of nationwide Kaohsiung college of technologies and Wroclaw college of expertise, and used to be held in Kaohsiung urban on November 10-12, 2010. ICCCI 2010 used to be technically co-sponsored through Shenzhen Graduate university 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 shopper Electronics. It aimed to collect researchers, engineers and po- cymakers to debate the similar recommendations, to replace examine principles, and to make associates. ICCCI 2010 all in favour of the subsequent issues: • Agent concept and alertness • Cognitive Modeling of Agent platforms • Computational Collective Intelligence • machine imaginative and prescient • Computational Intelligence • Hybrid structures • clever snapshot Processing • info Hiding • laptop studying • Social Networks • net Intelligence and interplay round 500 papers have been submitted to ICCCI 2010 and every paper was once reviewed by way of at the least referees. The referees have been from universities and business firms. a hundred and fifty five papers have been permitted for the ultimate technical software. 4 plenary talks have been kindly provided through: Gary G. Yen (Oklahoma country college, USA), on “Population keep watch over in Evolutionary Multi-objective Optimization Algorithm,” Chin-Chen Chang (Feng Chia collage, Taiwan), on “Applying De-clustering inspiration to details Hiding,” Qinyu Zhang (Harbin Institute of expertise, China), on “Cognitive Radio Networks and Its Applications,” and Lakhmi C.

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

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Active Contour External Force Using Vector Field Convolution For Image Segmentation. IEEE Transactions on Image Processing 16, 2096–2106 (2007) 2. : Nurbs Snakes. J. Image Vis. Comput. 21, 551–562 (2003) 3. : Affine-Invariant B-spline Moments For Curve Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 5, 1473–1480 (1996) 4. : Dynamic B-snake model for complex objects segmentation. J. Image Vis. Comput. 23, 1029–1040 (2005) 5. : Nonstationary Auto-regressive Modeling of Object Contours.

For a single illuminant, machine CC can be achieved none other than by adjusting the RGB channel gains of the image. Thus, a robust cast evaluation method needs to incorporate the scheme of the RGB gain variation. For visualization purpose, the gain variation is preferred in a scanning fashion. This is the first feature of the proposed CC algorithm. Equations 2 and 3 describe the relations of channel gain adjustment between two RGB images Io and I1, where α,β, and γare the RGB channel gain scaling coefficients, respectively.

SR,G,B(λ) is the spectral sensitivity of the RGB detector channel. After an image is captured by a RGB sensor, all the above parameters are factored in and difficult to decorrelate. The above sensor equations are extremely nonlinear. They do not have a significant role in the CC analysis. Human vision system adaptively adjusts the cone sensitivity of each channel to achieve chromatic adaptation. Similarly, for an RGB image, the color correction of an overall color cast is done by adjusting the RGB filters or channel gains.

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