Coarse-to-Fine Segmentation with Shape-Tailored Continuum Scale Spaces
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
KAUST Grant NumberOCRF-2014-CRG3-62140401
Online Publication Date2017-11-09
Print Publication Date2017-07
Permanent link to this recordhttp://hdl.handle.net/10754/626947
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AbstractWe 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.
CitationKhan 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.
SponsorsPartially funded by KAUST OCRF-2014-CRG3-62140401, NRF-2014R1A2A1A11051941, and NSF CCF-1526848.
Conference/Event name30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)