Soft Computing for Image Processing

By: Sankar K. Pal (Editor), Ashish Ghosh (Editor), Malay K. Kundu (Editor)

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Hardcover | 1 March 2000

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612 Pages

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23.5 x 15.88 x 2.54

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Image Processing Artificial Intelligence Engineering & Technology Other Technologies & Applied Sciences Applied Optics Computer Networking & Communications Graphical & Digital Media Applications Business & Management Business Mathematics & Systems

Electronics & Communications Engineering Communications Engineering & Telecommunications Business Applications

Foreword
Preface
Soft Computing and Image Analysis : Features, Relevance and Hybridization	p. 1
Image Filtering Using Evolutionary Neural Fuzzy Systems	p. 23
Edge Extraction Using Fuzzy Reasoning	p. 44
Image Compression and Edge Extraction Using Fractal Technique and Genetic Algorithm	p. 79
Adaptive Clustering for Efficient Segmentation and Vector Quantization of Images	p. 101
On Fuzzy Thresholding of Remotely Sensed Images	p. 130
Image Compression Using Pixel Neural Networks	p. 162
Genetic Algorithm and Fuzzy Reasoning for Digital Image Compression Using Triangular Plane Patches	p. 183
Compression of Digital Mammograms Using Wavelets and Fuzzy Algorithms for Learning Vector Quantization	p. 205
Soft Computing and Image Analysis	p. 246
Fuzzy Interpretation of Image Data	p. 260
New Pattern Recognition Tools Based on Fuzzy Logic for Image Understanding	p. 299
Adaptive, Evolving Hybrid Connectionist Systems for Image Pattern Recognition	p. 318
Neuro-Fuzzy Computing: Structure, Performance Measure and Applications	p. 337
Knowledge Reuse Mechanisms for Categorizing Related Image Sets	p. 375
Symbolic Data Analysis for Image Processing	p. 401
The Use of Artificial Neural Networks for Automatic Target Recognition	p. 431
Hybrid Systems for Facial Analysis and Processing Tasks	p. 473
Handwritten Digit Recognition Using Soft Computing Tools	p. 507
Neural Systems for Motion Analysis : Single Neuron and Network Approaches	p. 525
Motion Estimation and Compensation with Neural Fuzzy Systems	p. 552
About the Editors	p. 589
Table of Contents provided by Blackwell. All Rights Reserved.

Soft Computing for Image Processing

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