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    Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces

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    Khan_Coarse-to-Fine SegmentationWith Shape-Tailored Continuum Scale Spaces.pdf
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    Description:
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    Type
    Conference Paper
    Authors
    Khan, Naeemullah cc
    Hong, Byung-Woo
    Yezzi, Anthony
    Sundaramoorthi, Ganesh cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    KAUST Grant Number
    OCRF-2014-CRG3-62140401
    Date
    2017-11-09
    Online Publication Date
    2017-11-09
    Print Publication Date
    2017-07
    Permanent link to this record
    http://hdl.handle.net/10754/626947
    
    Metadata
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    Abstract
    We formulate an energy for segmentation that is designed to have preference for segmenting the coarse over fine structure of the image, without smoothing across boundaries of regions. The energy is formulated by integrating a continuum of scales from a scale space computed from the heat equation within regions. We show that the energy can be optimized without computing a continuum of scales, but instead from a single scale. This makes the method computationally efficient in comparison to energies using a discrete set of scales. We apply our method to texture and motion segmentation. Experiments on benchmark datasets show that a continuum of scales leads to better segmentation accuracy over discrete scales and other competing methods.
    Citation
    Khan N, Hong B-W, Yezzi A, Sundaramoorthi G (2017) Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available: http://dx.doi.org/10.1109/CVPR.2017.188.
    Sponsors
    Partially funded by KAUST OCRF-2014-CRG3-62140401, NRF-2014R1A2A1A11051941, and NSF CCF-1526848.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
    Conference/Event name
    30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    DOI
    10.1109/CVPR.2017.188
    Additional Links
    http://ieeexplore.ieee.org/document/8099671/
    http://openaccess.thecvf.com/content_cvpr_2017/html/Khan_Coarse-To-Fine_Segmentation_With_CVPR_2017_paper.html
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR.2017.188
    Scopus Count
    Collections
    Conference Papers; Electrical and Computer Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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