Notice of Violation of IEEE Publication Principles: A Gravitational Search based Fuzzy Approach for Edge Detection in Colour And Grayscale Images

Article Type

Research Article

Publication Title

IEEE Transactions on Fuzzy Systems


This article presents an optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and the Gravitational Search Algorithm (GSA). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the gray levels of every neighborhood pixel of a pixel of interest in the test image. In accordance with the SUSAN principle, the neighborhood is chosen as a circular mask and applied separately on the individual RGB components of the image in case the image is a color image. The USAN area edge map of each component is fuzzified using a Gaussian membership function (used for detecting strong edges) and a bell-shaped function (used for detecting weak edges). Then the entropy and edge sharpness factors are calculated from these fuzzy measures and optimized using GSA by evolving the fuzzifier and the parameters controlling the shape and range of the bell-shaped curve. Adaptive thresholding converts the fuzzy domain edge map to a spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors (both traditional as well as state-of-the-art) and also optimal fuzzy edge detectors based on metaheuristic algorithms like Differential Evolution (DE) and Particle Swarm Optimizer (PSO). Extensive comparisons based on several quantitative measures strongly reflect merits of the proposed method.



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