Research on Improved Canny Edge Detection Algorithm
Aiming at the poor noise robustness of traditional Canny algorithm and the defect of false edge or edge loss, an edge detection algorithm using statistical algorithm for filtering denoising and using genetic algorithm to determine the optimal high and low threshold of image segmentation is proposed....
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
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| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201823203053 |
| _version_ | 1818731921534877696 |
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| author | Liu Ruiyuan Mao Jian |
| author_facet | Liu Ruiyuan Mao Jian |
| author_sort | Liu Ruiyuan |
| collection | DOAJ |
| description | Aiming at the poor noise robustness of traditional Canny algorithm and the defect of false edge or edge loss, an edge detection algorithm using statistical algorithm for filtering denoising and using genetic algorithm to determine the optimal high and low threshold of image segmentation is proposed. Firstly, statistical filtering uses mean and variance to denoise, avoiding the problem of Gaussian denoising susceptible to interference in the traditional Canny algorithm, and ensuring the integrity of image edge information. Secondly, this article uses the genetic algorithm, design the crossover operator and genetic operator to modify the evolution of the population, and determine the optimal height threshold of the image edge connection to make the threshold more accurate. Finally, using MATLAB software to simulate, the results show that the improved Canny edge detection algorithm can further improve the anti-noise ability and robustness, and the edge location is more accurate. |
| first_indexed | 2024-12-17T23:25:21Z |
| format | Article |
| id | doaj.art-6fcfd754afeb4e8db590a838d877e474 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-17T23:25:21Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-6fcfd754afeb4e8db590a838d877e4742022-12-21T21:28:47ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012320305310.1051/matecconf/201823203053matecconf_eitce2018_03053Research on Improved Canny Edge Detection AlgorithmLiu Ruiyuan0Mao Jian1Shanghai University of Engineering Science, School Of Mechanical and Automotive EngineeringShanghai University of Engineering Science, School Of Mechanical and Automotive EngineeringAiming at the poor noise robustness of traditional Canny algorithm and the defect of false edge or edge loss, an edge detection algorithm using statistical algorithm for filtering denoising and using genetic algorithm to determine the optimal high and low threshold of image segmentation is proposed. Firstly, statistical filtering uses mean and variance to denoise, avoiding the problem of Gaussian denoising susceptible to interference in the traditional Canny algorithm, and ensuring the integrity of image edge information. Secondly, this article uses the genetic algorithm, design the crossover operator and genetic operator to modify the evolution of the population, and determine the optimal height threshold of the image edge connection to make the threshold more accurate. Finally, using MATLAB software to simulate, the results show that the improved Canny edge detection algorithm can further improve the anti-noise ability and robustness, and the edge location is more accurate.https://doi.org/10.1051/matecconf/201823203053 |
| spellingShingle | Liu Ruiyuan Mao Jian Research on Improved Canny Edge Detection Algorithm MATEC Web of Conferences |
| title | Research on Improved Canny Edge Detection Algorithm |
| title_full | Research on Improved Canny Edge Detection Algorithm |
| title_fullStr | Research on Improved Canny Edge Detection Algorithm |
| title_full_unstemmed | Research on Improved Canny Edge Detection Algorithm |
| title_short | Research on Improved Canny Edge Detection Algorithm |
| title_sort | research on improved canny edge detection algorithm |
| url | https://doi.org/10.1051/matecconf/201823203053 |
| work_keys_str_mv | AT liuruiyuan researchonimprovedcannyedgedetectionalgorithm AT maojian researchonimprovedcannyedgedetectionalgorithm |