| Summary: | 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
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