Download the full-sized PDF of Computer vision for computer-aided microfossil identificationDownload the full-sized PDF



Permanent link (DOI):


Export to: EndNote  |  Zotero  |  Mendeley


This file is in the following communities:

Graduate Studies and Research, Faculty of


This file is in the following collections:

Theses and Dissertations

Computer vision for computer-aided microfossil identification Open Access


Other title
Photometric Stereo
Visual Surface Estimation
Computerized Identification
Computer Vision
Type of item
Degree grantor
University of Alberta
Author or creator
Harrison, Adam
Supervisor and department
Joseph, Dileepan (Electrical and Computer Science)
Examining committee member and department
Zhao, H. Vicky (Electrical and Computer Science)
Jagersand, Martin (Computing Science)
Department of Electrical and Computer Engineering

Date accepted
Graduation date
Master of Science
Degree level
Micropalaeontology, a discipline that contributes to climate research and hydrocarbon exploration, is driven by the taxonomic analysis of huge volumes of microfossils. Unfortunately, this repetitive analysis is a serious bottleneck to progress because it depends on the scarce time of experts. These issues propel research into computerized taxonomic analysis, including a promising new approach called computer-aided microfossil identification. However, the existing computer-aided system relies on image-based representations, which severely limits its ability to discriminate specimens. These limitations motivate using computer vision to support richer video and shape-based representations, which is the focus of this thesis. An important contribution is a scheme to localize, capture, and extract video and shape-based representations from large microfossil batches. These representations encapsulate information across multiple lighting conditions. In addition, the thesis describes a method based on photometric stereo to correct misalignments in images of the same object illuminated from different directions. Not only does this correction benefit the application at hand, but it can also benefit a variety of other applications. The thesis also introduces a visual-surface reconstruction method based on maximum likelihood estimation, which constructs usable depth maps even from extraordinarily noisy images. State of the art methods lack this capability. By freeing classification from the bounds imposed by images, these contributions significantly advance computerized microfossil identification toward the ultimate goal of a practical and reliable tool for high-throughput taxonomic analysis.
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 these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before 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
Audit Status
Audits have not yet been run on this file.
File format: pdf (Portable Document Format)
Mime type: application/pdf
File size: 3146941
Last modified: 2015:10:12 12:19:20-06:00
Filename: Harrison_Adam_Spring2010.pdf
Original checksum: 04dec9d3f8d4d723e210773c3f593495
Well formed: false
Valid: false
Status message: Invalid page dictionary object offset=2729
File title: thesis.dvi
Activity of users you follow
User Activity Date