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- 저자 : Jiexin Pu (검색결과 3 건)
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A Multi-linear CCD Image Correction Method
Hanfei Kuang,Jiexin Pu,Lei Zhang,Zhonghua Liu,Bo Peng 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.10
With the aggravation of terrorist activities and frequent occuring of all kinds of car bomb attacks, social and public safety issues have become the focus of attention of the whole world. The vehicle chassis foreign body detection system developed in this paper is mainly used in all kinds of important occasions, to achieve real-time and safe detection of vehicles which is helpful to protect the lives and property of citizens, and to block the invasion and transimission of drugs and other harmful substances. Traditional vehicle chassis detection is carried out by security personnel with dedicated portable underbody detection tool, which is time-consuming, laborious and ineffective. In this paper, we construct a intelligent vehicle chassis image detection system. However, there is always distortion in the real time vehicle chassis image captured by multi-line CCD array which therefore needs to be corrected. This paper presents an image correction method for multi linear CCD. Firstly, Sobel differential operator is used to detect the binarized image of the edge of the vehicle chassis on horizontal and vertical directions. Secondly, we use Radon transform to detect angle of inclination of traveling distorted image captured by line scan cameras based on the results of edge detection, using shear transformation to correct binarized chassis image. Finally, a standard chassis image can be obtained based on image interpolation, the characteristic of Sobel operator and recapture the image. Experimental results show that this proposed method is simple and insensitive of stains and light. In addition, a standard chassis image can be obtained after completing the distortion correction.
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( Zhonghua Liu ),( Gang Liu ),( Jiexin Pu ),( Shigang Liu ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.5
In most of the existing literature, the definition of the class label has the following characteristics. First, the class label of the samples from the same object has an absolutely fixed value. Second, the difference between class labels of the samples from different objects should be maximized. However, the appearance of a face varies greatly due to the variations of the illumination, pose, and expression. Therefore, the previous definition of class label is not quite reasonable. Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper. In our algorithm, the maximization difference between the class labels of the samples from different objects should be satisfied. Meanwhile, the class label of the different samples from the same object is allowed to have small difference, which is consistent with the fact that the different samples from the same object have some differences. In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR). A large number of experiments show that our proposed algorithms can achieve very good performance.
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Robust Minimum Squared Error Classification Algorithm with Applications to Face Recognition
( Zhonghua Liu ),( Chunlei Yang ),( Jiexin Pu ),( Gang Liu ),( Sen Liu ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.1
Although the face almost always has an axisymmetric structure, it is generally not symmetrical image for the face image. However, the mirror image of the face image can reflect possible variation of the poses and illumination opposite to that of the original face image. A robust minimum squared error classification (RMSEC) algorithm is proposed in this paper. Concretely speaking, the original training samples and the mirror images of the original samples are taken to form a new training set, and the generated training set is used to perform the modified minimum sqreared error classification(MMSEC) algorithm. The extensive experiments show that the accuracy rate of the proposed RMSEC is greatly increased, and the the proposed RMSEC is not sensitive to the variations of the parameters.
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