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Wang, Hui.
Shape-guided interactive image segmentation /by Hui Wang.
Ph. D. -- University of Alberta, 2012
Edmonton, Alta. :University of Alberta,2012.
1 online resource
Title from PDF file main screen (viewed July 30, 2012).
A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computing Science, University of Alberta.
Includes bibliographical references.
This dissertation contributes to developing shape-guided algorithms for interactive image segmentation. Prior knowledge which describes what is expected in an image is the key to success for many challenging applications. This research takes advantage of prior knowledge in terms of shape priors, which is one of the most common object features, and user interaction, which is a part of many segmentation procedures to correct or bootstrap the method. In this research, shape-guided algorithms are developed for different types of interactive segmentation: initial segmentation, dealing with certain types of under-segmentation and over-segmentation mistakes, and final object boundary refinement. First, the adaptive shape prior method is developed in the graph cut framework to incorporate shape priors adaptively. After obtaining the initial segmentation, to deal with under-segmentation due to object fusion, the clump splitting method is proposed to take the advantage of shape information on the bottleneck position of the clumps. For over-segmentation which requires merging, the interactive merging method is implemented. Subsequently, to refine the incorrectly segmented object boundaries, the shape PCA method is developed to utilize statistical shape information when intensity information is inadequate. Shape information is embedded as the key in each of the proposed algorithms throughout the whole segmentation process. To integrate these proposed algorithms together, a comprehensive interactive segmentation system is developed which embeds five decisive tools: addition, deletion, splitting, merging and boundary refinement. By combining these tools, a state-of-the-art shape-guided interactive segmentation system can be constructed which is capable of extracting high quality foreground objects from images effectively and efficiently with minimal amount of user input.
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Image processing Digital techniques.
Pattern recognition systems.
Interactive computer systems.
Traitement d'images Techniques numériques.
Reconnaissance des formes (Informatique)
Systèmes conversationnels (Informatique)
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