Soft Computing for Image Processing

Artikelnummer: 978-3-7908-1268-8
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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.

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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VerlagPhysica
EinbandFester Einband
Erscheinungsjahr2000
Seitenangabe612 S.
AusgabekennzeichenEnglisch
MasseH24.1 cm x B16.0 cm x D3.8 cm 1'074 g
Auflage2000
AutorPal, Sankar K. (Hrsg.) / Kundu, Malay K. (Hrsg.) / Ghosh, Ashish (Hrsg.)

Über den Autor Sankar K. (Hrsg.) Pal

Sankar K. Pal (Life Fellow, IEEE) received the first Ph.D. degree in radio physics and electronics from the University of Calcutta, Kolkata, India, in 1979, and the second Ph.D. degree in electrical engineering along with DIC from Imperial College, University of London, London, UK, in 1982. He is currently National Science Chair, Government of India, and President of the Indian Statistical Institute (ISI). He is also Distinguished Scientist and Former Director of ISI, Former Distinguished Professor of the Indian National Science Academy, and Former Chair Professor of the Indian National Academy of Engineering. He founded the Machine Intelligence Unit and the Center for Soft Computing Research: a national facility in the institute in Calcutta. In 1975, he joined ISI as CSIR Senior Research Fellow where he became Full Professor in 1987, Distinguished Scientist in 1998, Director in 2005-2010, and President in 2022-2024. Sabu M. Thampi is a Professor at the School of Computer Science and Engineering, Digital University Kerala, Trivandrum, India. His current research interests include the Internet of Things (IoT), cognitive security, social networks, endpoint security, and smart cyber-physical systems. Sabu is also coordinating the Connected Systems and Intelligence (CSI) Lab at the University. He holds a Ph.D. in Computer Engineering from the National Institute of Technology Karnataka. Dr. Sabu has been actively involved in funded research projects and published papers in book chapters, journals, and conference proceedings. He has authored and edited a few books, as well as edited 45+ conference proceedings published by Springer in various series, as well as a few others published by IEEE, ACM, and Elsevier. Ajith Abraham received his Ph.D. in Computer Science from Monash University, Melbourne, Australia. He has a Master of Science in Control and Automation from Nanyang Technological University, Singapore. He holds a bachelor's degree in electrical and electronic engineering from the University of Calicut, Kerala, India. He has over 32 years of industry and academic experience. His primary research is on developing advanced machine intelligence using hybridization of function approximation methods, approximate reasoning and global optimization methods focused on big data analytics, understanding networks, information security, Web intelligence, decision support systems, the Internet of things, etc. He is Founding Director of Machine Intelligence Research Labs, a not-for-profit Scientific Network for Innovation and Research Excellence connecting industry and academia.

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