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Automatic segmentation algorithm of license plate image based on PCNN and DNN
Author(s): Xiangyu Deng; Wenjuan Qin; Ran Zhang; Yunping Qi
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Paper Abstract

License plate segmentation is a key technology in the process of license plate location and recognition. How to realize automatic segmentation of license plate image under complex illumination conditions has been a hot issue in intelligent transportation system (ITS). This paper deals with license plate image segmentation under a variety of lighting conditions. Based on the adaptive segmentation of license plate images by the Pulse Coupled Neural Network (PCNN), the relationship between the license plate image contrast and the PCNN iteration entropy is analyzed. An adaptive segmentation algorithm for license plate image using Deep Neural Network (DNN) to select the optimal result is proposed, and the selected segmentation image is filtered by the connected domain, which lays a foundation for subsequent license plate location, character segmentation and recognition. Simulation experiments show that the proposed algorithm performs better license plate segmentation and optimal selection for license plate images under various lighting conditions.

Paper Details

Date Published: 27 November 2019
PDF: 9 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 1132102 (27 November 2019); doi: 10.1117/12.2537280
Show Author Affiliations
Xiangyu Deng, Northwest Normal Univ. (China)
Wenjuan Qin, Northwest Normal Univ. (China)
Ran Zhang, Northwest Normal Univ. (China)
Yunping Qi, Northwest Normal Univ. (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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