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Permanent link (DOI): https://doi.org/10.7939/R3J932

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A Study of Match Cost Functions and Colour Use In Global Stereopsis Open Access

Descriptions

Other title
Subject/Keyword
parameter optimization
stereo vision
colour representations
stereopsis
global stereo
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Neilson, Daniel
Supervisor and department
Yang, Yee-Hong (Computing Science)
Examining committee member and department
Zhao, Vicky (Electrical and Computer Engineering)
Bischof, Walter (Computing Science)
Ray, Nilanjan (Computing Science)
Ferrie, Frank (Center for Intelligent Machines, McGill University)
Department
Department of Computing Science
Specialization

Date accepted
2009-07-02T19:08:27Z
Graduation date
2009-11
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Stereopsis is the process of inferring the distance to objects from two or more images. It has applications in areas such as: novel-view rendering, motion capture, autonomous navigation, and topographical mapping from remote sensing data. Although it sounds simple, in light of the effortlessness with which we are able to perform the task with our own eyes, a number of factors that make it quite challenging become apparent once one begins delving into computational methods of solving it. For example, occlusions that block part of the scene from being seen in one of the images, and changes in the appearance of objects between the two images due to: sensor noise, view dependent effects, and/or differences in the lighting/camera conditions between the two images. Global stereopsis algorithms aim to solve this problem by making assumptions about the smoothness of the depth of surfaces in the scene, and formulating stereopsis as an optimization problem. As part of their formulation, these algorithms include a function that measures the similarity between pixels in different images to detect possible correspondences. Which of these match cost functions work better, when, and why is not well understood. Furthermore, in areas of computer vision such as segmentation, face detection, edge detection, texture analysis and classification, and optical flow, it is not uncommon to use colour spaces other than the well known RGB space to improve the accuracy of algorithms. However, the use of colour spaces other than RGB is quite rare in stereopsis research. In this dissertation we present results from two, first of their kind, large scale studies on global stereopsis algorithms. In the first we compare the relative performance of a structured set of match cost cost functions in five different global stereopsis frameworks in such a way that we are able to infer some general rules to guide the choice of which match cost functions to use in these algorithms. In the second we investigate how much accuracy can be gained by simply changing the colour representation used in the input to global stereopsis algorithms.
Language
English
DOI
doi:10.7939/R3J932
Rights
License granted by Daniel Neilson (dneilson@cs.ualberta.ca) on 2009-06-27T03:13:27Z (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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