An Improved Canny Edge Detection Algorithm with Iteration Gradient Filter
Chen‐Hao Wu, Huijuan Ma, Hongqi Jiang, Zirui Huang, Zhengyue Cai, Ziying Zheng, Chin Hong Wong
Abstract
Edge detection is widely used in biological vision and computer vision. Canny edge detection is a common method to locate the sharp intensity changes and seek object boundaries. Nowadays, there are several improvement designs for Canny edge detection algorithms. In this paper, the Gaussian filtering is replaced by median filtering to increase the noise robustness of Canny edge detection. The inhabitant of the false edge is achieved by implementing an improved Sobel operator and iterative threshold filter method before the non-maximum suppression of the Canny operator. The final image is obtained after threshold filtering and binarization. The results show that the proposed algorithm is more robust to noise and can preserve more useful edge information by suppressing more false edges than traditional Canny edge detection.