• Login
    • Submit
    View Item 
    •   Repository Home
    • UT Electronic Theses and Dissertations
    • UT Electronic Theses and Dissertations
    • View Item
    • Repository Home
    • UT Electronic Theses and Dissertations
    • UT Electronic Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    The leaf identification problem : natural scene statistics and human performance

    Icon
    View/Open
    ING-DISSERTATION.pdf (4.333Mb)
    Date
    2010-05
    Author
    Ing, Almon David
    Share
     Facebook
     Twitter
     LinkedIn
    Metadata
    Show full item record
    Abstract
    For animals with advanced nervous systems, survival and reproduction can depend upon accurate perception of the environment. To understand how a perceptual system should solve a perception task, it is important to consider designs for an ideal observer, a theoretical system that solves a perception task in an optimal way given specific constraints. I studied three specific classification tasks related to the problem of identifying and segmenting leaves in foliage-rich images. In order to derive the ideal observers for these tasks, I created a database of hand-segmented leaves which served to define the ground-truth for these tasks. I also created a new method that uses the ground-truth as a basis for performing statistical inference (classification) in a nearly optimal way. This made it possible for me to approximate ideal observers by approximating an optimal classifier for each task. I also conducted psychophysical experiments to measure human performance in these tasks. The results provide information about how the human visual system should and does interpret foliage-rich images.
    Description
    text
    Subject
    Vision
    Optimal classification
    Modular visual system
    Perceptual system design
    Object identification
    URI
    http://hdl.handle.net/2152/ETD-UT-2010-05-784
    Collections
    • UT Electronic Theses and Dissertations
    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin

    Browse

    Entire RepositoryCommunities & CollectionsDate IssuedAuthorsTitlesSubjectsDepartmentThis CollectionDate IssuedAuthorsTitlesSubjectsDepartment

    My Account

    Login

    Information

    AboutContactPoliciesGetting StartedGlossaryHelpFAQs

    Statistics

    View Usage Statistics
    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin