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    Image Processing by Variational Methods, Stochastic Programming and Deep Learning Techniques

    Tan L 2020.pdf (40.26Mb)
    Access Status
    Open access
    Authors
    Tan, Lu
    Date
    2020
    Supervisor
    Ling Li
    Wan-Quan Liu
    Type
    Thesis
    Award
    PhD
    
    Metadata
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    Faculty
    Science and Engineering
    School
    School of Electrical Engineering, Computing and Mathematical Sciences
    URI
    http://hdl.handle.net/20.500.11937/82126
    Collection
    • Curtin Theses
    Abstract

    This thesis is to investigate effective approaches to tackle different problems in computer vision: variational methods are first studied for image processing, illusory contour reconstruction and segmentation as well as their efficiency improvement. Next, we develop variational segmentation methods by stochastic programming, tackling diverse problems with random noises. Third, the fusion approaches integrating varaitional models and deep neural networks are explored for challenging image tasks. These innovative ideas are validated by significant performance gains.

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