title: Type-2 fuzzy thresholding using GLSC histogram of human visual nonlinearity characteristics.
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.
authors.affiliation: National Key Laboratory of Science and Technology on Multi-spectral Information Processing, Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology,1037 Luoyu Road, Wuhan 430074, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Xiao
authors.forename: Zhiguo
authors.initials: Z
authors.lastname: Cao
authors.forename: Wen
authors.initials: W
authors.lastname: Zhuo
mesh_terms: D000465:Algorithms; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D015233:Models, Statistical; D055095:Optics and Photonics; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software; D014785:Vision, Ocular