http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Newly Regularized LDA for SSS Problem with application to Face Recognition
곽려혜(Lee Hui Kueh),김권우(Kwon-Woo Kim),한동열(Dong-Yeol Han),이승호(Seung-Ho Lee),이준탁(John-Tark Lee),이권순(Kwon-Soon Lee) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.7
A newly proposed FR (Face Recognition) approach with a weighted regularization parameter based on the conventional R-LDA (Regularized Linear D iscriminant A nalysis) method was presented in this paper. SSS (Sm all Sample Size) problem refers to the total number of training samples is less than the dimension of face feature space and all the scatter matrices of LDA are singular. Therefore, it is impossible to apply the LDA algorithm to the FR. In this paper, it was attempted to optimize the revised Fisher's criterion with a weighted regularization parameter as a solution of the SSS problem. Simulations using ORL (Olivetti Research Lab) database in MATLAB were simulated in order to evaluate the recognition performance of the proposed FR. In addition, the recognition performance of the proposed approach was compared to the ones of the well-known conventional methods such as Eigenfaces and R-LDA, which were established in this paper.
Detection of Copy-Move Image Forgery Based on SIFT-LBP
Kueh Lee Hui,Sonia Carole Kouayep 한국정보기술학회 2015 한국정보기술학회논문지 Vol.13 No.7
Here, we proposes a new view of an enhanced copy-move forgery detection using Local Binary Pattern (LBP) based on Scale Invariant Features Transform (SIFT). This framework computes rotating invariant subuniform local binary patterns from the image keypoints. The SIFT algorithm is first applied to the converted grayscale image from the original image to extract scale invariant keypoints of the image. Then, the subuniform LBP is computed centered at the keypoints to extract the feature vector of each keypoint. Finally, Chi-square histogram-based distance is computed for matching purpose to determine similarity. Besides that, Random Sample Consensus (RSC) algorithm is adopted in to this framework to remove mismatches. Our experiment results show that the proposed method can produce accurate detection results and exhibit high robustness to scale and rotation forged regions.
Blind Image Detection Based on Fusion Feature
Kueh Lee Hui,Sonia Carole Kouayep 한국정보기술학회 2016 한국정보기술학회논문지 Vol.14 No.4
Here, we proposed the fused feature based on the theoretical analysis and experimental verification of Local Binary Pattern (LBP) operator and Level Co-occurrence Matrix (GLCM) features to provide robust and complementary features than a single descriptor set for improving the detection performance of the blind image. This framework was focused on whether the image is blind or not by applying the fusion of two texture descriptor algorithms. The LBP algorithm is firstly employed to the image to create LBP image (LBP features). Then, the GLCM algorithm is applied to the LBP image to extract local co-occurrence features. The fused features both gain spatial distribution from the LBP image and the co-occurrence features. Finally, Support Vector Machine (SVM) is computed for forgery detecting on the image. The experiment results show that the proposed framework can achieve the accuracy of 98.5% on color image dataset of CASIA database.
Face Recognition Using Newly Regularized LDA
Lee Hui Kueh,John Tark Lee,Kwon Soon Lee 한국정보기술학회 2010 한국정보기술학회논문지 Vol.8 No.6
A newly proposed FR (Face Recognition) approach with a weighted regularization parameter based on the conventional R-LDA (Regularized Linear Discriminant Analysis) method was presented in this paper. In the case of SSS (Small Sample Size), since the face feature space has typically a large number of pixels and the total number of training samples is less than the dimension of face feature space, all the scatter matrices of LDA are singular and its recognition accuracy is directly deteriorated. Therefore, it is impossible to apply the origina1 LDA algorithm to the FR. In this paper, it was attempted to optimize the revised Fisher's criterion with a weighted regularization parameter as a solution of the SSS problem. ORL (Olivetti Research Lab) database by using MATLAB was analyzed and the performance of face recognition was evaluated. The simulations were given by identifying the test samples as the some of the training subjects. In addition, the recognition performance of the proposed approach was compared to the ones of the well-known conventional methods such as Eigenfaces and R-LDA, which were established in this paper. The main interest idea of this paper was to demonstrate the superiority in robustness against the SSS problem of the proposed approach to the conventional Eigenfaces and R-LDA methods. The recognition rate of the proposed approach were fairly higher than others. In the future, the applicability of this proposed algorithm to the FR of the large sample database shall be discussed.
Lee Hui Kueh,John-Tark Lee,Kwon-Soon Lee 한국지능정보시스템학회 2009 한국지능정보시스템학회 학술대회논문집 Vol.2009 No.11
A face recognition based on Regularized LDA is presented. The recognition performance is demonstrated in Matlab using face database of Intelligent Control Laboratory (ICONL). The algorithm attempts to address the SSS problem using a regularized Fisher's separability criterion. The simulations are conducted which identified the test samples as belonging to the same subject of those of trained subject of the ICONL face database. In addition, the recognition performance is compared to the recognition performance of the original PCA which are demonstrated in the paper. The main idea of the paper is to demonstrate the proposed method is particularly robust against the SSS problem compared to the traditional LDA and the simulations results show that the proposed algorithm improves the robustness of the recognition performance compared to PCA.
이승호(Seung-Ho Lee),곽려혜(Lee-Hui Kueh),김권우(Kwon-Woo Kim),한동열(Dong-yeol Han),이준탁(John-Tark Lee) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.7
This paper is design of synchronous machine using matlab. I did description of a basic attribute numerical formula of a synchronous machine. I used matlab in the above and calculated basic data of the synchronous machine. The input data which I calculated with matlab is the voltage, frequency, power factor, power. and, the output data calculated design size data and data of an efficiency, loss of etc.
A Study on Face Recognition Using PCA
Joon-Tark Lee,Lee-Hui Kueh 동아대학교 정보기술연구소 2006 情報技術硏究所論文誌 Vol.14 No.1
In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory (ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.
A Study on the Face Recognition Using PCA
Joon-Tark Lee,Lee Hui Kueh 한국지능시스템학회 2006 한국지능시스템학회 학술발표 논문집 Vol.16 No.2
In this paper, a face recognition algorithm system using Principle Component Analysis is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals which is a face database of Intelligence Control Laboratory (ICONL). Experiments were simulated in order to demonstrate the performance of this algorithm due to face recognition which presented for the classification of face and non-face and the classification of known and unknown.
A Study on the Face Recognition Using PCA Algorithm
이준탁(John-Tark Lee),곽려혜(Lee-Hui Kueh) 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.2
In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.