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A BAYESIAN FRAMEWORK OF 2D IMAGE CORRESPONDENCE AND 3D
STRUCTURE-MOTION ESTIMATION
by
Shuang Wu
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PHYSICS AND ASTRONOMY)
May 2008
Copyright 2008 Shuang Wu
Object Description
| Title | A Bayesian framework of 2D image correspondence and 3D |
| Author | Wu, Shuang |
| Author email | shuangw@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Physics |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2007-11-12 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-02-11 |
| Advisor (committee chair) | von der Malsburg, Christoph |
| Advisor (committee member) |
Haas, Stephan Dappen, Werner Zanardi, Paolo Itti, Laurent |
| Abstract | Structure-motion problem, the problem of estimating 3D shape of objects and their motions based on 2D image sequence, is a fundamental problem in vision research. While many biological visual systems have perception of 3D structure and motion from 2D visual stimuli, computer algorithms with similar capabilities and performance are still been sought after.; In part I of this thesis, conventional approaches to structure-motion problem were reviewed. Their underlying principles were outlined and the difficulties they face explained.; In part II, a Bayesian framework was proposed to address the structure-motion problem. First, a concept of probabilistic image correspondence inspired by difficulties facing conventional one-to-one image correspondence was proposed and formulated in a sequential Monte Carlo perspective. Then the same perspective was applied again to formulate structure-from-motion problem as a process of recursive estimation. Finally, a complete theoretical framework of structure-motion problem was constructed by combining probabilistic image correspondence and Bayesian structure-from-motion.; In part III, am implementation of this Bayesian framework was tested on artificial data. The results were subsequently examined and discussed. |
| Keyword | structure; motion; tracking; Bayesian |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m1014 |
| Rights | Wu, Shuang |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Wu-20080211 |
| Archival file | uscthesesreloadpub_Volume35/etd-Wu-20080211.pdf |
Description
| Title | Page 1 |
| Full text | A BAYESIAN FRAMEWORK OF 2D IMAGE CORRESPONDENCE AND 3D STRUCTURE-MOTION ESTIMATION by Shuang Wu A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (PHYSICS AND ASTRONOMY) May 2008 Copyright 2008 Shuang Wu |
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