Constraint Self-Adaptive SUSAN Algorithm for Edge Detection

For the traditional smallest univalue segment assimilating nucleus(SUSAN)algorithm in edge detection,the fixed critical value will consider the non-edge points to be within the univalue segment assimilating nucleus(USAN)and judge by the threshold,resulting in the misjudgment of the non-edge points as edge points and low noise robustness.To this end,we propose a constraint self-adaptive SUSAN algorithm combining the adaptive critical value algorithm and threshold selection strategy.Firstly,the threshold selection strategy is set according to the characteristics of the USAN and the noise tolerance to reduce misjudgment and improve the noise robustness.Then an adaptive critical value algorithm with a positive correlation with the pixel value in USAN is used to enhance the edge detection capability.Compared with the traditional SUSAN algorithm and Canny,Prewitt,Sobel,LoG,Roberts algorithms,the experimental results show that the proposed method has higher precision,FSIM,and PFOM in the qualitative measurement.In the case that lots of noise interferences cause the failure of other algorithms,the proposed algorithm can effectively detect the image edge while suppressing the noise.