Edge detection of gear image based on Canny operator and morphology

CHEN Shun, LI Deng-feng
(School of Mathematics and Computer, Wuhan Textile University, Wuhan 430200, China)

Abstract: Aiming at the problem that it is difficult to effectively suppress noise and accurately detect more real edges in the edge detection of noisy gear images, the improved Canny operator and mathematical morphology algorithm were applied to the edge detection of noisy gear images, and a fusion was proposed based on Canny operator and mathematical morphology of the noisy image edge detection algorithm. Firstly, the improved Canny operator edge detection was used, then the multiscale multistructure mathematical morphology edge detection was applied. Then, the two edge images were waveletdecomposed to obtain the sublayer images. Finally, the subimages were adaptively weighted and used.The image was reconstructed by inverse wavelet transform to obtain the final edge detection image. The experimental results show that the fusion algorithm is better than the improved Canny operator, the mathematical morphology denoising effect, the positioning accuracy is high, the edge is continuous and clear, and it has good denoising effect when the noise concentration increases. It is a feasible unsupervised fusion algorithm.
Key words: noisy gear image; Canny operator; mathematical morphology; wavelet decomposition