BP Neural Network Fuse with Morphology Edge Detection Method
In order to obtain better image edge information, an edge detection algorithm combining BP (Back Propagation) neural network and morphology is proposed. The Sigmoid function is commonly used as the excitation function in BP neural networks, but the traditional Sigmoid function is single in form an...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2021-10-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2018 |
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| _version_ | 1849710331983560704 |
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| author | YUE Xin-hua DENG Cai-xia ZHANG Zhao-ru |
| author_facet | YUE Xin-hua DENG Cai-xia ZHANG Zhao-ru |
| author_sort | YUE Xin-hua |
| collection | DOAJ |
| description | In order to obtain better image edge information, an edge detection algorithm combining BP (Back
Propagation) neural network and morphology is proposed. The Sigmoid function is commonly used as the excitation
function in BP neural networks, but the traditional Sigmoid function is single in form and lacks flexibility.
Therefore, it is very important to provide an adjustable Sigmoid function. First, a fully smooth Sigmoid function
construction method is given, which is used as the excitation function in the BP neural network to detect the edge of
the image effectively. Then, using the idea of multi-scale and multi-structure, an improved morphological edge
detection algorithm is proposed, and the edge image with small noise and continuous is obtained by applying this
algorithm. Finally, the wavelet analysis is used to fuse the BP neural network and the improved morphological
algorithm, and then an edge detection fusion algorithm is obtained. The simulation results show that the evaluation
index of the fusion algorithm is better than a single edge detection algorithm and the detected image edge lines are
complete and clear. |
| format | Article |
| id | doaj-art-10fd5aa07fda43e1a899ff1333a21821 |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2021-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-10fd5aa07fda43e1a899ff1333a218212025-08-20T03:14:57ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832021-10-012605839010.15938/j.jhust.2021.05.011BP Neural Network Fuse with Morphology Edge Detection Method YUE Xin-hua0DENG Cai-xia1ZHANG Zhao-ru2School of Applied Sciences, Harbin Unjversily of Science and Technology, Harbin 150080, ChinaSchool of Applied Sciences, Harbin Unjversily of Science and Technology, Harbin 150080, ChinaSchool of Applied Sciences, Harbin Unjversily of Science and Technology, Harbin 150080, ChinaIn order to obtain better image edge information, an edge detection algorithm combining BP (Back Propagation) neural network and morphology is proposed. The Sigmoid function is commonly used as the excitation function in BP neural networks, but the traditional Sigmoid function is single in form and lacks flexibility. Therefore, it is very important to provide an adjustable Sigmoid function. First, a fully smooth Sigmoid function construction method is given, which is used as the excitation function in the BP neural network to detect the edge of the image effectively. Then, using the idea of multi-scale and multi-structure, an improved morphological edge detection algorithm is proposed, and the edge image with small noise and continuous is obtained by applying this algorithm. Finally, the wavelet analysis is used to fuse the BP neural network and the improved morphological algorithm, and then an edge detection fusion algorithm is obtained. The simulation results show that the evaluation index of the fusion algorithm is better than a single edge detection algorithm and the detected image edge lines are complete and clear.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2018back propagation neural networksigmoid functionmathematical morphologyedge detectionfusion algorithm |
| spellingShingle | YUE Xin-hua DENG Cai-xia ZHANG Zhao-ru BP Neural Network Fuse with Morphology Edge Detection Method Journal of Harbin University of Science and Technology back propagation neural network sigmoid function mathematical morphology edge detection fusion algorithm |
| title | BP Neural Network Fuse with Morphology Edge Detection Method |
| title_full | BP Neural Network Fuse with Morphology Edge Detection Method |
| title_fullStr | BP Neural Network Fuse with Morphology Edge Detection Method |
| title_full_unstemmed | BP Neural Network Fuse with Morphology Edge Detection Method |
| title_short | BP Neural Network Fuse with Morphology Edge Detection Method |
| title_sort | bp neural network fuse with morphology edge detection method |
| topic | back propagation neural network sigmoid function mathematical morphology edge detection fusion algorithm |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2018 |
| work_keys_str_mv | AT yuexinhua bpneuralnetworkfusewithmorphologyedgedetectionmethod AT dengcaixia bpneuralnetworkfusewithmorphologyedgedetectionmethod AT zhangzhaoru bpneuralnetworkfusewithmorphologyedgedetectionmethod |