Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics |
Optics Express, Vol. 19, Issue 11, pp. 10656-10672 (2011)
http://dx.doi.org/10.1364/OE.19.010656
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Abstract
Image thresholding is one of the most important approaches for image segmentation and it has been extensively used in many image processing or computer vision applications. In this paper, a new image thresholding method is presented using type-2 fuzzy sets based on GLSC histogram of human visual nonlinearity characteristics (HVNC).The traditional GLSC histogram takes the image spatial information into account in a different way from two-dimensional histogram. This work refines the GLSC histogram by embedding HVNC into GLSC histogram. To select threshold based on the redefined GLSC histogram, we employ the type-2 fuzzy set, whose membership function integrates the effect of pixel gray value and local spatial information to membership value. The type-2 fuzzy set is subsequently transformed into a type-1 fuzzy set for fuzziness measure computation via type reduction. Finally, the optimal threshold is obtained by minimizing the fuzziness of the type-1 fuzzy set after an exhaustive search. The experiment on different types of images demonstrates the effectiveness and the robustness of our proposed thresholding technique.
© 2011 OSA
OCIS Codes
(100.0100) Image processing : Image processing
(100.2000) Image processing : Digital image processing
(100.3008) Image processing : Image recognition, algorithms and filters
ToC Category:
Image Processing
History
Original Manuscript: March 3, 2011
Revised Manuscript: May 7, 2011
Manuscript Accepted: May 10, 2011
Published: May 16, 2011
Virtual Issues
Vol. 6, Iss. 6 Virtual Journal for Biomedical Optics
Citation
Yang Xiao, Zhiguo Cao, and Wen Zhuo, "Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics," Opt. Express 19, 10656-10672 (2011)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-19-11-10656
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