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Background Estimation with GPU Speed Up Open Access


Other title
Background Estimation
Type of item
Degree grantor
University of Alberta
Author or creator
Chen, Xida
Supervisor and department
Yee-Hong Yang, Computing Science
Examining committee member and department
Mario A. Nascimento, Computing Science
Arie Croitoru, Earth & Atmospheric Sciences
Department of Computing Science

Date accepted
Graduation date
Master of Science
Degree level
Given a set of images from the same viewpoint, in which occlusions are present, background estimation is to output an image with stationary objects in the scene only. Background estimation is an important step in many computer vision problems such as object detection and recognition. With the growing interest in more sophisticated video surveillance systems, the requirement for the accuracy of background estimation increases as well. In this thesis, we present two novel methods whose fundamental objectives are the same, namely, to estimate the background of a set of related images. In order to make our methods more general, we assume that the input images can be taken either from the same viewpoint or from different viewpoints. Both methods combine information from multiple input images by selecting the appropriate pixels to construct the background. Our first method is a scanline energy optimization method, and our second method is based on graph cuts optimization. We apply these two methods to datasets with different feature and the results are encouraging. Furthermore, we use the CUDA (Compute Unified Device Architecture) programming language to make full use of the GPU processing power. GPU stands for Graphics Processing Unit, which employs parallel processing and is more powerful than the CPU. In particular, we implement an efficient graph-based image segmentation algorithm as well as a linear blending method using the CUDA programming language for acceleration, both of which are used in our first method. The speedup of our GPU implementation can be 20 times faster.
License granted by Xida Chen ( on 2009-09-10T20:01:23Z (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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