Intelligent Computing in Signal Processing and Pattern by Chun-Dong Wang, Ming Gao, Xiu-Feng Wang (auth.), De-Shuang

By Chun-Dong Wang, Ming Gao, Xiu-Feng Wang (auth.), De-Shuang Huang, Kang Li, George William Irwin (eds.)

" clever Computing in sign Processing and trend popularity " constitutes the result of the foreign convention on clever Computing, ICIC 2006, held in Kunming, China, in August 2006. The rigorously reviewed contributions are integrated in 5 volumes of edited books, together with one quantity of Lecture Notes in machine technological know-how (LNCS), one quantity of Lecture Notes in man made Intelligence (LNAI), one quantity of Lecture Notes in Bioinformatics (LNBI), and volumes of Lecture Notes up to the mark and knowledge Sciences (LNCIS). The 149 revised complete papers of this quantity have been reviewed and chosen from over 3000 submissions, overlaying the sector of clever Computing in sign Processing and trend acceptance, in addition to a different consultation on Computing for looking out options to manage Dynamic Processes.

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IEEE Trans. Med. Imaging 18 (11) (1999) 1085-1097. ca/brainweb 17. Kennedy, D. N, Filipek, P. A, Caviness, V. S: Anatomic Segmentation and Volumetric Calculations in Nuclear Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, 8 (1989) 1-7. edu/ibsr/ 18. : Brain Segmentation and White Matter Lesion Detection in MR Images. Crit. Rev. Biomed. Eng. 22(5–6) (1994) 401–465 19. Karen, C. R. Lin, M. , Yang, H. , Lirng, Wang, P. : Generalized Kohonen’s Competitive Learning Algorithm for Ophthalmological MR Image Segmentation.

Mag Reson Imaging 18 (2000) 1245–1253 15. : Self-Organizing Maps. New York: Springer-Verlag (1995) 16. Kwan, R. , Pike, G: MRI Simulation-based Evaluation of Image-processing and Classification Methods. IEEE Trans. Med. Imaging 18 (11) (1999) 1085-1097. ca/brainweb 17. Kennedy, D. N, Filipek, P. A, Caviness, V. S: Anatomic Segmentation and Volumetric Calculations in Nuclear Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging, 8 (1989) 1-7. edu/ibsr/ 18. : Brain Segmentation and White Matter Lesion Detection in MR Images.

In general, there are two types of polygonal approximation problems which have attracted many researchers’ interest. They are described as follows: Min-# problem: Given a closed digital curve, approximate it by a polygon with a minimum number of line segments such that the approximation error does not exceed a given tolerance error ε. Min-ε problem: Given a closed digital curve, approximate it by a polygon with a given number of line segments such that the approximation error is minimized. Corresponding author.

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