ERA

Download the full-sized PDF of Background Estimation with GPU Speed UpDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R3PS81

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Background Estimation with GPU Speed Up Open Access

Descriptions

Other title
Subject/Keyword
GPU
Background Estimation
Type of item
Thesis
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
Department of Computing Science
Specialization

Date accepted
2009-09-16T18:04:58Z
Graduation date
2009-11
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3PS81
Rights
License granted by Xida Chen (xida@cs.ualberta.ca) 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.
Citation for previous publication

File Details

Date Uploaded
Date Modified
2014-05-01T01:12:04.277+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 11855891
Last modified: 2015:10:12 13:19:42-06:00
Filename: Chen_Xida_Fall 2009.pdf
Original checksum: fb2f9d1e7e4e9e871ed45db54c94269c
Well formed: true
Valid: true
File title: titlewithout
File title: University of Alberta
File author: Faculty of Graduate Studies and Research
Page count: 109
Activity of users you follow
User Activity Date