Adaptive Canny edge detection based on fast median filtering

Zhuang Zhang; Yifeng Zhu

doi:10.1117/12.3048320

3 October 2024 Adaptive Canny edge detection based on fast median filtering

Zhuang Zhang, Yifeng Zhu

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720I (2024) https://doi.org/10.1117/12.3048320
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

Aiming at the problem that the original Canny algorithm is incomplete in detecting the edges of the image and is very sensitive to the pretzel noise and the small changes in the edge gradient, this paper proposes an edge detection algorithm based on the improved adaptive Canny algorithm. Firstly, a fast median filter is utilized in place of a Gaussian filter to accomplish the image denoising process. Next, the convolution template of the traditional Canny algorithm is extended to four directions to determine the magnitude of the gradient in order to prevent the loss of edge details. Finally, it uses maximum interclass variance (Otsu) to adaptively select the dual thresholds, which improves the accuracy. Experimental results demonstrate that the algorithm presented in this paper achieves superior edge integrity and noise reduction. with fewer pseudo edges, the denoising quality improves by 15-20% and the edge evaluation index improves by 20–25%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhuang Zhang and Yifeng Zhu "Adaptive Canny edge detection based on fast median filtering", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720I (3 October 2024); https://doi.org/10.1117/12.3048320

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KEYWORDS

Tunable filters

Digital filtering

Gaussian filters

Edge detection

Image filtering

Detection and tracking algorithms

Denoising

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