An Improved Canny Algorithm with Adaptive Threshold Selection

Abstract

Canny is a classic algorithm of edge detection which has been widely applied in various fields of image processing for years. However, the algorithm has some defects. The most serious defect is that the traditional canny algorithm can’t set threshold adaptively. If the threshold set manually is not accurate, it will seriously affect the quality of the algorithm to detect the edge. This makes the poor adaptability of the algorithm. This paper proposes a method which combines maximum entropy method with Otsu method to determine the high and low threshold of Canny algorithm. Experiments show that the modified algorithm has stronger robustness than traditional method. For the images which have complex distributions of grey level histogram, the modified algorithm has better performance.