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RISS 인기검색어
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- 저자 : ( Peiqin Li ) (검색결과 4 건)
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An Algorithm for Vein Matching Based on Log-polar Transform
Wei Yan,Jianbin Xie,Peiqin Li,Tong Liu,Xiaoguang Guo 한국산학기술학회 2013 SmartCR Vol.3 No.5
This article discusses a new algorithm for vein matching based on log-polar transform to address problems that occur with the changing of finger position and from differences between imaging devices for current vein matching algorithms. The new algorithm first extracts the feature area, which contains enough characteristics for image matching, depending on the structure of the finger vein ridge alignment. It then calculates the degree of similarity between the log-polar transform results of the model image feature areas and the sample image, and finally analyzes the result by the degree of similarity and the relationship of relative positions between feature areas. Experiments show that the algorithm is robust for rotating and zooming images of the finger vein.
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Driver`s Face Detection Using Space-time Restrained Adaboost Method
( Tong Liu ),( Jianbin Xie ),( Wei Yan ),( Peiqin Li ) 한국인터넷정보학회 2012 KSII Transactions on Internet and Information Syst Vol.6 No.9
Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver`s face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver`s face can be detected rapidly and precisely.
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A Fast and Robust Algorithm for Fighting Behavior Detection Based on Motion Vectors
( Jianbin Xie ),( Tong Liu ),( Wei Yan ),( Peiqin Li ),( Zhaowen Zhuang ) 한국인터넷정보학회 2011 KSII Transactions on Internet and Information Syst Vol.5 No.11
In this paper, we propose a fast and robust algorithm for fighting behavior detection based on Motion Vectors (MV), in order to solve the problem of low speed and weak robustness in traditional fighting behavior detection. Firstly, we analyze the characteristics of fighting scenes and activities, and then use motion estimation algorithm based on block-matching to calculate MV of motion regions. Secondly, we extract features from magnitudes and directions of MV, and normalize these features by using Joint Gaussian Membership Function, and then fuse these features by using weighted arithmetic average method. Finally, we present the conception of Average Maximum Violence Index (AMVI) to judge the fighting behavior in surveillance scenes. Experiments show that the new algorithm achieves high speed and strong robustness for fighting behavior detection in surveillance scenes.
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Finger Vein Representation by Modified Binary Tree Model
Tong Liu,Jianbin Xie,Huanzhang Lu,Wei Yan,Peiqin Li 한국산학기술학회 2013 SmartCR Vol.3 No.2
Finger vein recognition has high identification accuracy and strong security performance, which can be used in banks, offices, factories, etc. Although image representation is not a necessary process for finger vein recognition, a proper representation method can help to explore distribution regularities and structure differences of finger veins, and provides instructive information for finger vein recognition. It is very difficult to represent finger veins because of their irregular structure. Therefore, four principles (caliber uniformity, node replication, loop splitting, and virtual connection) are proposed in this paper, first to simplify the finger vein structure as a binary tree structure. Then a modified binary tree model is proposed based on the binary tree structure. The new model uses the binary tree to describe the relationships between different vein branches and uses a B-spline function to describe the spatial structure of vein branches. Experiments show that this model can quantitatively describe the relationships between, and the spatial structure of, vein branches with little representation error and low storage space requirements.
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