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    Building an Intelligent Knowledgebase of Brachiopod Paleontology

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    Meng_ku_0099M_12989_DATA_1.pdf (4.412Mb)
    ppt slides for the thesis (5.538Mb)
    Issue Date
    2013-08-31
    Author
    Meng, Yuanliang
    Publisher
    University of Kansas
    Format
    78 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
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    Abstract
    Science advances not only because of new discoveries, but also due to revolutionary ideas drawn from accumulated data. The quality of studies in paleontology, in particular, depends on accessibility of fossil data. This research builds an intelligent system based on brachiopod fossil images and their descriptions published in Treatise on Invertebrate Paleontology. The project is still on going and some significant developments will be discussed here. This thesis has two major parts. The first part describes the digitization, organization and integration of information extracted from the Treatise. The Treatise is in PDF format and it is non-trivial to convert large volumes into a structured, easily accessible digital library. Three important topics will be discussed: (1) how to extract data entries from the text, and save them in a structured manner; (2) how to crop individual specimen images from figures automatically, and associate each image with text entries; (3) how to build a search engine to perform both keyword search and natural language search. The search engine already has a web interface and many useful tasks can be done with ease. Verbal descriptions are second-hand information of fossil images and thus have limitations. The second part of the thesis develops an algorithm to compare fossil images directly, without referring to textual information. After similarities between fossil images are calculated, we can use the results in image search, fossil classification, and so on. The algorithm is based on deformable templates, and utilizes expectation propagation to find the optimal deformation. Specifically, I superimpose a warp on each image. Each node of the warp encapsulates a vector of local texture features, and comparing two images involves two steps: (1) deform the warp to the optimal configuration, so the energy function is minimized; and (2) based on the optimal configuration, compute the distance of two images. Experiment results confirmed that the method is reasonable and robust.
    URI
    http://hdl.handle.net/1808/12184
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3828]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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