Abstract
The gradual change edge of the Si3N4 bearing roller microcracks with decreasing gray gradients are difficult to be extracted by threshold segmentation. The method for extracting the gradual change edge of Si3N4 bearing roller microcracks using adaptive non-local mean filtering and an iterative tracking algorithm is proposed. Widely distributed, large-span, dense noise is eliminated from the gradual change edges of microcracks in Si3N4 bearing roller microcrack images. In the iterative expansion process of microcrack defect shape, the gradual change edge pixel of microcrack is accurately tracked. The Si3N4 bearing roller microcrack image is enhanced by adaptive non-local mean filtering. The following are the experimental findings: The PSNR and SNR reach 38.12 dB and 40.94 dB, respectively. The microcrack gradual change edge pixels can be extracted with an edge coverage rate of 92.5 % and an accuracy of 93.8 % using the iterative tracking algorithm for Si3N4 bearing roller microcrack images.
Published Version
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