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    Shape-Tailored Features and their Application to Texture Segmentation

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    Name:
    Naeemullah_Khan_Thesis.pdf
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    10.78Mb
    Format:
    PDF
    Description:
    Naeem - final thesis
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    Type
    Thesis
    Authors
    Khan, Naeemullah cc
    Advisors
    Sundaramoorthi, Ganesh cc
    Committee members
    Alouini, Mohamed-Slim cc
    Wonka, Peter cc
    Program
    Electrical Engineering
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Date
    2014-04
    Permanent link to this record
    http://hdl.handle.net/10754/317258
    
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    Abstract
    Texture Segmentation is one of the most challenging areas of computer vision. One reason for this difficulty is the huge variety and variability of textures occurring in real world, making it very difficult to quantitatively study textures. One of the key tools used for texture segmentation is local invariant descriptors. Texture consists of textons, the basic building block of textures, that may vary by small nuisances like illumination variation, deformations, and noise. Local invariant descriptors are robust to these nuisances making them beneficial for texture segmentation. However, grouping dense descriptors directly for segmentation presents a problem: existing descriptors aggregate data from neighborhoods that may contain different textured regions, making descriptors from these neighborhoods difficult to group, leading to significant errors in segmentation. This work addresses this issue by proposing dense local descriptors, called Shape-Tailored Features, which are tailored to an arbitrarily shaped region, aggregating data only within the region of interest. Since the segmentation, i.e., the regions, are not known a-priori, we propose a joint problem for Shape-Tailored Features and the regions. We present a framework based on variational methods. Extensive experiments on a new large texture dataset, which we introduce, show that the joint approach with Shape-Tailored Features leads to better segmentations over the non-joint non Shape-Tailored approach, and the method out-performs existing state-of-the-art.
    Citation
    Khan, N. (2014). Shape-Tailored Features and their Application to Texture Segmentation. KAUST Research Repository. https://doi.org/10.25781/KAUST-TP035
    DOI
    10.25781/KAUST-TP035
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-TP035
    Scopus Count
    Collections
    MS Theses; Electrical and Computer Engineering Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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