Soft computing in mechatronics
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Bibliographic Information
Soft computing in mechatronics
（Studies in fuzziness and soft computing, v. 42）
Physica-Verlag, c2000
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Includes bibliographical references
Description and Table of Contents
Description
Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc.
Table of Contents
S.K. Pal, A. Ghosh, M.K. Kundu: Soft Computing and Image Analysis: Features, Relevance and Hybridization.- Preprocessing and Feature Extraction: F.Russo: Image Filtering Using Evolutionary Neural Fuzzy Systems.- T. Law, D. Shibata, T. Nakamura, L. He, H. Itoh: Edge Extraction Using Fuzzy Reasoning.- S.K. Mitra, C.A. Murthy, M.K. Kundu: Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithms.- S. Mitra, R. Castellanos, S.-Y. Yang, S. Pemmaraju: Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images.- B. Uma Shankar, A. Ghosh, S.K. Pal: On Fuzzy Thresholding of Remotely Sensed Images.- W. Skarbek: Image Compression Using Pixel Neural Networks.- L He, Y. Chao, T. Nakamura, H. Itho: Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches.- N B. Karayiannis, T.C. Wang: Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization.- V.D. Gesu: Soft Computing and Image Analysis.- J.H. Han, T.Y. Kim, L.T. Koczy: Fuzzy Interpretation of Image Data.- Classification: M. Grabisch: New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding.- N.K. Kasabov, S.I. Israel, B.J. Woodford: Adaptive, Evolving, Hybrid Connectionist Systems for Image Pattern Recognition.- P.A. Stadter, N.K Bose: Neuro-Fuzzy Computing: Structure, Performance Measure and Applications.- K. D. Bollacker, J. Ghosh: Knowledge Reuse Mechanisms for Categorizing Related Image Sets.- K. C. Gowda, P. Nagabhushan, H.N. Srikanta Prakash: Symbolic Data Analysis for Image Processing.- Applications: N.M. Nasrabadi, S. De, L.-C. Wang, S. Rizvi, A. Chan: The Use of Artificial Neural Networks for Automatic Target Recognition.- S. Gutta, H. Wechsler:Hybrid Systems for Facial Analysis and Processing Tasks.- V. Susheela Devi, M. Narasimha Murty: Handwritten Digit Recognition Using Soft Computing Tools.- T.L. Huntsburger, J.R. Rose, D. Girard: Neural Systems for Motion Analysis: Single Neuron and Network Approaches.- H.M. Kim, B. Kosko: Motion Estimation and Compensation with Neural Fuzzy Systems.
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