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http://dx.doi.org/10.9708/jksci/2012.17.12.071

License Plate Detection with Improved Adaboost Learning based on Newton's Optimization and MCT  

Lee, Young-Hyun (Dept. of Visual Information Processing, Korea University)
Kim, Dae-Hun (School of Electrical Engineering, Korea University)
Ko, Han-Seok (School of Electrical Engineering, Korea University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.12, 2012 , pp. 71-82 More about this Journal
Abstract
In this paper, we propose a license plate detection method with improved Adaboost learning and MCT (Modified Census Transform). The MCT represents the local structure patterns as integer numbered feature values which has robustness to illumination change and memory efficiency. However, since these integer values are discrete, a lookup table is needed to design a weak classifier for Adaboost learning. Some previous research efforts have focused on minimization of exponential criterion for Adaboost optimization. In this paper, a method that uses MCT and improved Adaboost learning based on Newton's optimization to exponential criterion is proposed for license plate detection. Experimental results on license patch images and field images demonstrate that the proposed method yields higher performance of detection rates with low false positives than the conventional method using the original Adaboost learning.
Keywords
License plate detection; Adaboost learning; Newton's optimization; MCT;
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Times Cited By KSCI : 2  (Citation Analysis)
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