Edge detection algorithm of Canny based on maximum between-class posterior probability
一种基于最大类间后验概率的Canny边缘检测算法

Based on the analysis of the traditional Canny algorithm, the adaptive filter took the place of the original Gaussian filter and made use of cross-entropy to measure the differences between the background and objectives. Combining Bayesian judgment theory, the average cross-entropy of posterior probability of the pixels of original image to objective and background areas presented differences between classes, and this paper maximized the posterior probability to judge pixels in which different regions to obtain the optimal level of the threshold. The experimental results show the improved algorithm has great edge detection effect.