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Computer vision for computer-aided microfossil identification

  • Author / Creator
    Harrison, Adam
  • 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.

  • Subjects / Keywords
  • Graduation date
    2010-06
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3107T
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Electrical and Computer Engineering
  • Supervisor / co-supervisor and their department(s)
    • Joseph, Dileepan (Electrical and Computer Science)
  • Examining committee members and their departments
    • Zhao, H. Vicky (Electrical and Computer Science)
    • Jagersand, Martin (Computing Science)