Thousands of high-resolution images are generated each day. Segmenting, classifying, and analyzing the contents of these images are the key steps in image understanding. This thesis focuses on image segmentation and classification and its applications in synthetic, texture, natural, biomedical, and industrial images. A robust level-set-based multi-region and texture image segmentation approach is proposed in this thesis to tackle most of the challenges in the existing multi-region segmentation methods, including computational complexity and sensitivity to initialization.
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- Partial requirement for: Ph.D., Arizona State University, 2010Note typethesis
- Includes bibliographical references (p. 142-159)Note typebibliography
- Field of study: Electronic engineering