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    Urban Image Analysis with Convolutional Sparse Coding

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    ThesisReport_LamaAffara.pdf
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    Description:
    Dissertation
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    Type
    Dissertation
    Authors
    Affara, Lama Ahmed cc
    Advisors
    Wonka, Peter cc
    Committee members
    Heidrich, Wolfgang cc
    Ghanem, Bernard cc
    Wright, John
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2018-09-18
    Permanent link to this record
    http://hdl.handle.net/10754/628738
    
    Metadata
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    Abstract
    Urban image analysis is one of the most important problems lying at the intersection of computer graphics and computer vision research. In addition, Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. This dissertation handles urban image analysis using an asset extraction framework, studies CSC for the reconstruction of both urban and general images using supervised data, and proposes a better computational approach to CSC. Our asset extraction framework uses object proposals which are currently used for increasing the computational efficiency of object detection. In this dissertation, we propose a novel adaptive pipeline for interleaving object proposals with object classification and use it as a formulation for asset detection. We first preprocess the images using a novel and efficient rectification technique. We then employ a particle filter approach to keep track of three priors, which guide proposed samples and get updated using classifier output. Tests performed on over 1000 urban images demonstrate that our rectification method is faster than existing methods without loss in quality, and that our interleaved proposal method outperforms current state-of-the-art. We further demonstrate that other methods can be improved by incorporating our interleaved proposals. We also extend the applicability of the CSC model by proposing a supervised approach to the problem, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data. We finally present two computational contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as RGB images and videos. Our results show up to 20 times speedup compared to current state-of-the-art CSC solvers.
    Citation
    Affara, L. A. (2018). Urban Image Analysis with Convolutional Sparse Coding. KAUST Research Repository. https://doi.org/10.25781/KAUST-CPY94
    DOI
    10.25781/KAUST-CPY94
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-CPY94
    Scopus Count
    Collections
    PhD Dissertations; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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