Shared more. Cited more. Safe forever.
    • advanced search
    • submit works
    • about
    • help
    • contact us
    • login
    View Item 
    •   MOspace Home
    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Theses (MU)
    • 2008 Theses (MU)
    • 2008 MU theses - Freely available online
    • View Item
    •   MOspace Home
    • University of Missouri-Columbia
    • Graduate School - MU Theses and Dissertations (MU)
    • Theses and Dissertations (MU)
    • Theses (MU)
    • 2008 Theses (MU)
    • 2008 MU theses - Freely available online
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    advanced searchsubmit worksabouthelpcontact us

    Browse

    All of MOspaceCommunities & CollectionsDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis SemesterThis CollectionDate IssuedAuthor/ContributorTitleIdentifierThesis DepartmentThesis AdvisorThesis Semester

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular AuthorsStatistics by Referrer

    Content extraction, analysis, and retrieval for plant visual traits studies

    Hao, Dayang
    View/Open
    [PDF] public.pdf (1.996Kb)
    [PDF] short.pdf (30.03Kb)
    [PDF] research.pdf (7.251Mb)
    Date
    2008
    Format
    Thesis
    Metadata
    [+] Show full item record
    Abstract
    In recent years, automatic content extraction, analysis and retrieval for plant visual trait studies play a more important role than ever before, because traditional manual processing suffers two major issues: excessive processing time and subjectiveness rising from different individuals. Therefore, to conduct high throughput experiments, plant biologists are in urgent need for, 1) efficient computer software to automatically extract and analyze significant contents, 2) scoring functions to mimic human scoring, etc. In order to meet these needs, a series of customized computer vision and image processing algorithms is developed in our research. These algorithms are particularly customized for two model plants: Maize lesion extraction and Arabidopsis insect damage calculation. For maize, a mixture of edge-based and region-based segmentation algorithm is developed to extract lesions caused by fungus; for Arabidopsis, a top-down segmentation process is employed to measure leaf area differences resulting from insect damage. Our informatics tools can be generalized to accommodate a broad range of plant species for visual trait studies. Moreover, they will potentially provide a framework for cross-institutional and collaborative studies.
    URI
    https://doi.org/10.32469/10355/5704
    https://hdl.handle.net/10355/5704
    Degree
    M.S.
    Thesis Department
    Computer science (MU)
    Rights
    OpenAccess.
    This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.
    Collections
    • 2008 MU theses - Freely available online
    • Computer Science electronic theses and dissertations (MU)

    Send Feedback
    hosted by University of Missouri Library Systems
     

     


    Send Feedback
    hosted by University of Missouri Library Systems