Image Edge Detecting Based on Gap Statistic Model and Relative Entropy

Based on gap statistic model and relative entropy theory, a new edge detecting algorithm is presented in this paper. Firstly, to get the gap membership functions, a dimensional various gap plane is established by calculating gap value per pixel. Then, according to the relative entropy theory, a relative entropy coefficient decision threshold is obtained. Finally, using criterion function algorithm on the gap plane, edge information is extracted. Experimental results indicate that, compared with the classical Sobel edge operator, the proposed algorithm is not only efficient in extracting edge information but also better in de-noising performance.