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Adaptive local threshold with shape information and its application to oil sand image segmentation Open Access


Other title
shape information
Type of item
Degree grantor
University of Alberta
Author or creator
Shi, Jichuan
Supervisor and department
Zhang, Hong (Computing Science)
Examining committee member and department
Zhao, H. Vicky (Electrical and Computer Engineering)
Ray, Nilanjan (Computing Science)
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
This thesis is concerned with a novel local threshold segmentation algorithm for digital images incorporating shape information. In image segmentation, most local threshold algorithms are based only on intensity analysis. In many applications where an image contains objects with a similar shape, in addition to the intensity information, some prior known shape attributes could be exploited to improve the segmentation. The goal of this work is to design a local threshold algorithm that includes shape information to enhance the segmentation quality. The algorithm adaptively selects a local threshold. Shape attribute distributions are learned from typical objects in ground truth images. Local threshold for each object in an image to be segmented is chosen to maximize probabilities in these shape attributes distributions. Then for the application of the oil sand image segmentation, a supervised classifier is introduced to further enhance the segmentation accuracy. The algorithm applies a supervised classifier trained by shape features to reject unwanted fragments. To meet different image segmentation intents in practical applications, we investigate a variety of combination of shape attributes and classifiers, and also look for the optimal one. Experiments on oil sand images have shown that the proposed algorithm has superior performance to local threshold approaches based on intensity information in terms of segmentation quality.
License granted by Jichuan Shi ( on 2010-01-06T20:30:03Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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