Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/934182

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Title
Cascade-based license plate localization with line segment features and Haar-like features
Author/Creator
Peng, Yu;  Xu, Min;  Jin, Jesse S.;  Luo, Suhuai;  Zhao, Gang
Description
AdaBoost classifiers with Haar-like features are widely used for license plate (LP) localization. However, it normally requires high-dimensional Haar-like features which cause extremely high computational cost. In this paper, a rejection cascade was built for LP localization with reduced Haar-like features. We first introduced line segment features as pre-input of Haar-like features for AdaBoost to eliminate more than 70% of the background in an image. Line segment features, including density, directionality and regularity, were extracted from line segments, which were detected by applying Hough Transform on an edge image. Later, AdaBoost classifiers with Haar-like features were further applied to identify the exact location of license plates. Our method dramatically reduced the demanded dimensions of Haar-like features, therefore saved much time in AdaBoost training stage. By comparing our method with methods of only using Haar-like features and only using line segment features, experimental results demonstrated that our proposed method achieved the best detection rate with significantly reduced dimensions of Haar-like features.
Relation
6th International Conference on Image and Graphics 2011, (ICIG 2011). Proceedings of the 6th International Conference on Image and Graphics, ICIG 2011 (Hefei, China 12-15 August, 2011) p. 1023-1028
Publisher Link
http://dx.doi.org/10.1109/ICIG.2011.154
Date
2011
Publisher
IEEE Computer Society
Keyword(s)
Haar transforms;  Hough transforms;  adaptive systems;  edge detection;  feature extraction
Resource Type
conference paper
Identifier
http://hdl.handle.net/1959.13/934182
Identifier
ISBN:9781457715600
Language
eng
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Subject
"Cascade-based license plate localization with line segment features and Haar-like features"
 
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