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License plate detection in an open environment by density-based boundary clustering

[+] Author Affiliations
Jiangmin Tian, Guoyou Wang, Jianguo Liu, Yuanchun Xia

Huazhong University of Science and Technology, State Key Laboratory for Multispectral Information Processing Technology, School of Automation, Wuhan, China

J. Electron. Imaging. 26(3), 033017 (May 27, 2017). doi:10.1117/1.JEI.26.3.033017
History: Received January 5, 2017; Accepted April 11, 2017
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Abstract.  Due to the variation of background, illumination, and view point, license plate detection in an open environment is challenging. We propose a detection method by boundary clustering. To start with, a boundary map is obtained through Canny edge detector and removal of unwanted horizontal background edges. Second, boundaries are classified into different clusters by a density-based approach. In the approach, the density of each boundary is defined by the total gradient intensity of its neighboring and reachable boundaries. Also, the cluster centers and the number of them are determined automatically according to a minimum-distance principle. At last, a set of horizontal candidate regions with accurately located borders are extracted for classification. The classifier is trained on the histogram of oriented gradient feature by a linear support vector machine model. Experiments on three public datasets including images captured under different scenarios demonstrate that the proposed method outperforms several state-of-the-art methods in detection accuracy and its performance in efficiency is also comparable.

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Citation

Jiangmin Tian ; Guoyou Wang ; Jianguo Liu and Yuanchun Xia
"License plate detection in an open environment by density-based boundary clustering", J. Electron. Imaging. 26(3), 033017 (May 27, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.3.033017


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