Patchlets : a method of interpreting correlation stereo 3D data
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Murray, Donald R.
issued: 2009-11-30T22:15:00Z
issued: 2003
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: This thesis describes methods for fitting local planar surface elements, that we call patchlets, to 3D data obtained from correlation stereo images. We use these patchlets to robustly extract and estimate bounded planar surfaces from complex and noisy stereo scenes. The patchlet element is a small planar surface the size of a pixel projected onto a world surface. It has a position, a normal direction, a size, and confidence metrics on its position and orientation. The confidence metrics are generated from the noise model of the stereo vision system, which are propagated to 3D point data and then to the patchlet parameters. The patchlets are used to extract larger bounded planar surfaces that are useful for environment modeling. We use a region-growing approach to identify how many surfaces exist in a stereo image and an initial estimate of the surface parameters. We then use Expectation-Maximisation (EM) to refine these surface parameters to an optimal estimate using a probability maximisation approach. The confidence metrics of the patchlet parameters allow proper weighting of patchlet contributions to the probability maximisation solution. We verify by experimental means the accuracy of correlation stereo matching and demonstrate that the patchlet confidence metrics obtained from that accuracy fit expected normal distributions. We compare the results of the patchlet-based surface segmentation to manually constructed ground-truth segmentation and find the segmentation accuracy for a scene ranged from 82% to 93%. We also present a method for filtering noise from correlation stereo disparity images.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/15995
extent: 44129344 bytes
format: application/pdf
note: Patchlets : a method of interpreting correlation stereo 3D data by Donald R. Murray B.Sc. Elec. Eng., University of Alberta, 1987 M.Sc. Elec. Eng., University of Alberta, 1992 A THESIS SUBMITTED IN PARTIAL F U L F I L L M E N T OF T H E REQUIREMENTS FOR THE D E G R E E OF Doc to r of Phi losophy in T H E FACULTY OF GRADUATE STUDIES (Department of Computer Science) We accept this thesis as conforming to the required standard The University of British Columbia December 2003 © Donald R. Murray, 2003 A b s t r a c t This thesis describes methods for fitting local planar surface elements, that we call patchlets, to 3D data obtained from correlation stereo images. We use these patchlets to robustly extract and estimate bounded planar surfaces from complex and noisy stereo scenes. The patchlet element is a small planar surface the size of a pixel projected onto a world surface. It has a position, a normal direction, a size, and confidence metrics on its position and orientation. The confidence metrics are generated from the noise model of the stereo vision system, which are propagated to 3D point data and then to the patchlet parameters. The patchlets are used to extract larger bounded planar surfaces that are useful for environment modeling. We use a region-growing approach to identify how many surfaces exist in a stereo image and an initial estimate of the surface parameters. We then use Expectation-Maximisation (EM) to refine these surface parameters to an optimal estimate using a probability maximisation approach. The confidence metrics of the patchlet parameters allow proper weighting of patchlet contributions to the probability maximisation solution. We verify by experimental means the accuracy of correlation stereo matching and demonstrate that the patchlet confidence metrics obtained from that accuracy fit expected normal distributions. We compare the results of the patchlet-based surface segmentation to manually constructed ground-truth segmentation and find the s…
has type: Thesis/Dissertation
date issued: 2003-11
is shown at: 10.14288/1.0051745
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/15995