Application of fuzzy set theoty in image processing

• Main Authors: Lee ,Yih-Gong, 李懿恭
• Other Authors: Yuang-Cheh Hsueh
• Format: Others
• Language: en_US
• Published: 1996
• Online Access: http://ndltd.ncl.edu.tw/handle/13880625212268425291

博士 === 國立交通大學 === 資訊科學學系 === 84 === Image processing has been a fast-growing field for the last thirty years. Influence for its growth and advancement has arisen from studies in artificial intelligence, psychology, psychophysics, computer architecture and computer graphics. Application area for image processing includes document processing, medicine and physiology, remote sensing, industrial automation and surveillance amongst many others. Image process- ing involves various operations on image data. These operations include preprocessing, spatial filtering, image enhancement, feature detection, image compression, image restoration, and so on. However, uncertainty abounds in most phases of image processing. It is natural and also appropriate to define primitives and relation among them using labels of fuzzy set. Fuzzy set theory has been widely used in science and industry because of its capability to model nonstatistical imprecision. The conventional quantitative techniques of system analysis are unsuited for dealing with humanistic systems and other compar- able complex systems, because, as the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes until a threshold is reached beyond which precision and significance (or relevance) become almost mutually exclusive characteristics [Zadeh 1973]. In this thesis, we focus on the image enhancement, edge detection and texture analysis by using fuzzy uncertainty and fuzzy logic methods. The techniques of image enhancement include smoothing, interpolation and sharpening. Edge detection the fundamental importance task in image processing. Texture analysis is an import technique in image processing because plays a critical role in inspecting surfaces and provides important techniques in a variety of applications ranging from medical imaging to remote sensing. We also use the popular method of genetic algorithms to obtain more flexible membership functions to avoid the ill-defined membership