Fuzzy Image Processing and Applications with MATLAB

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9781439807088: Fuzzy Image Processing and Applications with MATLAB

In contrast to classical image analysis methods that employ "crisp" mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge.

Fuzzy Image Processing and Applications with MATLAB® presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few.

Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation.

Minimize Processing Errors Using Dynamic Fuzzy Set Theory

This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision―whether it is in the grey level of the image, geometry of an object, definition of an object’s edges or boundaries, or in knowledge representation, object recognition, or image interpretation.

The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others.

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About the Author:

Tamalika Chaira received her bachelor’s degree in electronics and communication from Bihar Institute of Technology, Sindri, India; her master’s degree in electronics and communication from BE College, Shibpur; and her Ph.D in image processing from the Indian Institute of Technology, Kharagpur, India, in 1993, 2000, and 2003, respectively. Her research interests include image processing, fuzzy logic, intuitionistic fuzzy logic, and medical information processing. She has the following achievements to her credit: 1 Intel U.S. patent, 10 papers in international journals, several book chapters, and several papers in international conferences. She is listed in the International Biographic Centre, Cambridge, United Kingdom, and Marquis Who’s Who in Science and Engineering, United States. She is a reviewer of several IEEE journals. She is also a member of Soft Computing in Image Processing. Currently, she is a young scientist in the Department of Science and Technology working at the Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, India.

Professor Ajoy Kumar Ray is currently the vice-chancellor of Bengal Engineering and Science University, Howrah, India. He received his BE in electronics and telecommunication engineering from Bengal Engineering College, Sibpur, India, and his MTech and Ph.D from the Indian Institute of Technology (IIT), Kharagpur, India. Prior to this, he was the head of the School of Medical Science and Technology and a professor of electronics and electrical communication engineering at IIT Kharagpur. Professor Ray has successfully completed 17 research projects as principal investigator, sponsored by Intel Corporation, Texas Instruments, in addition to those funded by agencies such as Defense Research and Development Organization (DRDO), Department of Science and Technology (DST), the Department of Atomic Energy, and the Department of Information Technology, India. He was at the University of Southampton during 1989-1990 and was the head of the research division at Avisere, Inc., United States, during 2004-2005. He has coauthored four books published by international publishing houses. He is the coinventor of six U.S. patents filed jointly with Intel Corporation, as well as three patents filed jointly with Texas Instruments. He has coauthored more than 90 research papers in international journals and conferences. As secretary and chairman of the Nehru Museum of Science and Technology, Professor Ray has conceptualized and created several galleries on science and technology. His research interests include image processing, machine intelligence, soft computing, and molecular imaging in disease detection.

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Book Description Taylor Francis Inc, United States, 2009. Hardback. Book Condition: New. Language: English . Brand New Book. In contrast to classical image analysis methods that employ crisp mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge. Fuzzy Image Processing and Applications with MATLAB(R) presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few. Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation. Minimize Processing Errors Using Dynamic Fuzzy Set Theory This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision-whether it is in the grey level of the image, geometry of an object, definition of an object s edges or boundaries, or in knowledge representation, object recognition, or image interpretation. The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others. Bookseller Inventory # AAZ9781439807088

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Book Description Taylor Francis Inc, United States, 2009. Hardback. Book Condition: New. Language: English . Brand New Book. In contrast to classical image analysis methods that employ crisp mathematics, fuzzy set techniques provide an elegant foundation and a set of rich methodologies for diverse image-processing tasks. However, a solid understanding of fuzzy processing requires a firm grasp of essential principles and background knowledge. Fuzzy Image Processing and Applications with MATLAB(R) presents the integral science and essential mathematics behind this exciting and dynamic branch of image processing, which is becoming increasingly important to applications in areas such as remote sensing, medical imaging, and video surveillance, to name a few. Many texts cover the use of crisp sets, but this book stands apart by exploring the explosion of interest and significant growth in fuzzy set image processing. The distinguished authors clearly lay out theoretical concepts and applications of fuzzy set theory and their impact on areas such as enhancement, segmentation, filtering, edge detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing, threshold detection, and match-based segmentation. Minimize Processing Errors Using Dynamic Fuzzy Set Theory This book serves as a primer on MATLAB and demonstrates how to implement it in fuzzy image processing methods. It illustrates how the code can be used to improve calculations that help prevent or deal with imprecision-whether it is in the grey level of the image, geometry of an object, definition of an object s edges or boundaries, or in knowledge representation, object recognition, or image interpretation. The text addresses these considerations by applying fuzzy set theory to image thresholding, segmentation, edge detection, enhancement, clustering, color retrieval, clustering in pattern recognition, and other image processing operations. Highlighting key ideas, the authors present the experimental results of their own new fuzzy approaches and those suggested by different authors, offering data and insights that will be useful to teachers, scientists, and engineers, among others. Bookseller Inventory # AAZ9781439807088

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