In this paper, a new method for license plate detection based on AdaBoost is proposed. In the new method, character
frequency feature, which is powerful feature for detecting license plate character, are introduced to feature pool. The
frequency features obtained from the FFT of horizontal projection of binary image are selected by AdaBoost. Then,
Haar-like features selected by AdaBoost are used to capture subtle structure of license plate. Furthermore, considering
the characteristic of Chinese license plate that there are two types of license plate: deeper background-lighter character
and lighter background-deeper character license plates, two detectors are designed to extract different license plates
respectively. Experimental results show the efficiency of the proposed method.
In this paper, a new method of facial expression recognition is proposed for missing tensor data. In this method, the
missing tensor data is estimated by facial expression synthesis in order to construct the full tensor, which is used for
multi-factorization face analysis. The full tensor data allows for the full use of the information of a given database, and
hence improves the performance of face analysis. Compared with EM algorithm for missing data estimation, the
proposed method avoids iteration process and reduces the estimation complexity. The proposed missing tensor data
estimation is applied for expression recognition. The experimental results show that the proposed method is performing
better than only utilize the original smaller tensor.
Conference Committee Involvement (1)
Parallel Processing for Imaging Applications II
23 January 2012 | Burlingame, California, United States
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