• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.435-438
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• 2008

본 논문에서는 애니메이션을 위해서 얼굴의 특징표현(Action Units)의 조합하는 방법으로 얼굴 모델링을 하기 위한 3D대응점(3D dense correspondence)을 찾는 방법을 제시한다. AUs는 표정, 감정, 발음을 나타내는 얼굴의 특징표현으로 통계적 방법인 PCA (Principle Component Analysis)를 이용하여 만들 수 있다. 이를 위해서는 우선 3D 모델상의 대응점을 찾는 것이 필수이다. 2D에서 얼굴의 주요 특징 점은 다양한 알고리즘을 이용하여 찾을 수 있지만 그것만으로 3D상의 얼굴 모델을 표현하기에는 적합하지 않다. 본 논문에서는 3D 얼굴 모델의 대응점을 찾기 위해 원기둥 좌표계 (Cylinderical Coordinates System)을 이용하여 3D 모델을 2D로 투사(Projection)시켜서 만든 2D 이미지간의 워핑(Warping) 을 통한 대응점을 찾아 역으로 3D 모델간의 대응점을 찾는다. 이것은 3D 모델 자체를 변환하는 것보다 적은 연산량으로 계산할 수 있고 본래 형상의 변형이 없다는 장점을 가지고 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.91-96
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• 2015

In this study, we introduce robust face recognition system with illumination variation realized with the aid of CT preprocessing method. As preprocessing algorithm, Census Transform(CT) algorithm is used to extract locally facial features under unilluminated condition. The dimension reduction of the preprocessed data is carried out by using $(2D)^2$PCA which is the extended type of PCA. Feature data extracted through dimension algorithm is used as the inputs of proposed radial basis function neural networks. The hidden layer of the radial basis function neural networks(RBFNN) is built up by fuzzy c-means(FCM) clustering algorithm and the connection weights of the networks are described as the coefficients of linear polynomial function. The essential design parameters (including the number of inputs and fuzzification coefficient) of the proposed networks are optimized by means of artificial bee colony(ABC) algorithm. This study is experimented with both Yale Face database B and CMU PIE database to evaluate the performance of the proposed system.

• The Journal of Fisheries Business Administration
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• v.14 no.2
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• pp.1-14
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• 1983

This paper considers a classification of the 88 islands located at Kyong-nam area in Korea, using by examples of 12 components of the islands. By means of principal component analysis 2 principle components were extracted, which explained a total of 73.7% of the variance. Using an eigen variable criterion (λ>1), no further principle components were discussed. Principal component 1 and 2 explained 63.4% and 10.3% of the total variance respectively, The representation of the unrelated factor scores along the first and second principal axes produced a new information with respect to the classification of the islands. Based upon the representation, 88 islands were classified into 6 groups i. e. A, B, C, D, E, and F according to similarity of the components among them in this paper. The "Group F" belongs to a miscellaneous assortment that does not fit into the logical category. category.

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.2
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• pp.31-40
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• 2012

Face recognition refers to the process of identifying individuals based on their facial features. It has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous consumer applications, such as access control, surveillance, security, credit-card verification, and criminal identification. However, illumination variation on face generally cause performance degradation of face recognition systems under practical environments. Thus, this paper proposes an novel face recognition system using a fusion approach based on local binary pattern and two-dimensional principal component analysis. To minimize illumination effects, the face image undergoes the local binary pattern operation, and the resultant image are divided into two sub-images. Then, two-dimensional principal component analysis algorithm is separately applied to each sub-images. The individual scores obtained from two sub-images are integrated using a weighted-summation rule, and the fused-score is utilized to classify the unknown user. The performance evaluation of the proposed system was performed using the Yale B database and CMU-PIE database, and the proposed method shows the better recognition results in comparison with existing face recognition techniques.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.5
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• pp.413-418
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• 2003

Genetic diversity and soybean sprout-related traits were evaluated in a total of 72 soybean accessions (60 Glycine max, 7 Glycine soja, and 5 Glycine gracilis). 100-seed weight (SW) was greatly varied and ranged from 3.2g to 32.3g in 72 soybean accessions. Positive correlation was observed between GR and hypocotyl length (HL), whereas negative correlation was observed between SW and hypocotyl diameter (HD). Re-evaluation by discarding two soybean genotypes characterized with low GR indicated that much higher correlation of sprout yield (SY) with HD and SW. Based on the principal component analysis (PCA) for sprout-related traits, 57 accessions were classified. Soybean genotypes with better traits for sprout, such as small size of seeds and high SY, were characterized with high PCA 1 and PCA 2 values. The seed size in second is small but showed low GR and SY, whereas the third has large seed, high GR and more than 400% SY. In genetic similarity analysis using 60 SSR marker genotyping, 72 accessions were classified into three major and several minor groups. Nine of twelve accessions that were identified as the representatives of soybean for sprout based on PCA were in a group by the SSR marker analysis, indicating the SSR marker selection of parental genotypes for soybean sprout improvement program.

• Journal of Broadcast Engineering
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• v.25 no.7
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• pp.1095-1106
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• 2020

In this paper, we propose an algorithm that extracts a high-precision 3D skeleton using a model generated using a distributed RGB-D camera. When information about a 3D model is extracted through a distributed RGB-D camera, if the information of the 3D model is used, a skeleton with higher precision can be obtained. In this paper, in order to improve the precision of the 2D skeleton, we find the conditions to obtain the 2D skeleton well using the PCA. Through this, high-quality 2D skeletons are obtained, and high-precision 3D skeletons are extracted by combining the information of the 2D skeletons. Even though this process goes through, the generated skeleton may have errors, so we propose an algorithm that removes these errors by using the information of the 3D model. We were able to extract very high accuracy skeletons using the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Journal of Microbiology and Biotechnology
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• v.22 no.1
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• pp.69-79
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• 2012

In an ongoing survey of the bioactive potential of microorganisms from Ladakh, India, the culture medium of a bacterial strain of a new Pseudomonas sp., strain ICTB-745, isolated from an alkaline soil sample collected from Leh, Ladakh, India, was found to contain metabolites that exhibited broad-spectrum antimicrobial and biosurfactant activities. Bioactivity-guided purification resulted in the isolation of four bioactive compounds. Their chemical structures were elucidated by $^1H$ and $^{13}C$ NMR, 2D-NMR (HMBC, HSQC, $^1H$,$^1H$-COSY, and DEPT-135), FT-IR, and mass spectroscopic methods, and were identified as 1-hydroxyphenazine, phenazine-1-carboxylic acid (PCA), rhamnolipid-1 (RL-1), and rhamnolipid-2 (RL-2). These metabolites exhibited various biological activities like antimicrobial and efficient cytotoxic potencies against different human tumor cell lines such as HeLa, HepG2, A549, and MDA MB 231. RL-1 and RL-2 exhibited a dose-dependent antifeedant activity against Spodoptera litura, producing about 82.06% and 73.66% antifeedant activity, whereas PCA showed a moderate antifeedant activity (63.67%) at 60 ${\mu}g/cm^2$ area of castor leaf. Furthermore, PCA, RL-1, and RL-2 exhibited about 65%, 52%, and 47% mortality, respectively, against Rhyzopertha dominica at 20 ${\mu}g/ml$. This is the first report of rhamnolipids as antifeedant metabolites against Spodoptera litura and as insecticidal metabolites against Rhyzopertha dominica. The metabolites from Pseudomonas sp. strain ICTB-745 have interesting potential for use as a biopesticide in pest control programs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.19-29
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• 2011

Until now, many face recognition methods have been proposed, most of them use a 1-dimensional feature vector which is vectorized the input image without feature extraction process or input image itself is used as a feature matrix. It is known that the face recognition methods using raw image yield deteriorated performance in databases whose have severe illumination changes. In this paper, we propose a face recognition method using local statistics of gradients and correlations which are good for illumination changes. BDIP (block difference of inverse probabilities) is chosen as a local statistics of gradients and two types of BVLC (block variation of local correlation coefficients) is chosen as local statistics of correlations. When a input image enters the system, it extracts the BDIP, BVLC1 and BVLC2 feature images, fuses them, obtaining feature matrix by $(2D)^2$ PCA transformation, and classifies it with training feature matrix by nearest classifier. From experiment results of four face databases, FERET, Weizmann, Yale B, Yale, we can see that the proposed method is more reliable than other six methods in lighting and facial expression.

• Journal of the Korean Society of Tobacco Science
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• v.16 no.2
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• pp.163-171
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• 1994

This experiment was conducted to investigate the discrimination of cigarette brands and the similarity between Korea and America cigarette brands by near infrared spectra. Statistical tools such as principal component analysis (PCA) and mahalanobis distance(M.D) were used. The discrimination rate of the Korea and the America cigarette brands, determined by position number which was calculated with PCA and M.D, was 94% and 87%, respectively. The spectra of the 10 America cigarette brands were selected by averaging 5 sample spectra for each brand and another 5 spectra for each brand were investigated to use as the sample spectra. Comparing the sample spectra with the selected spectra by M.D using 410-1090 nm, 1110-1850 nm and 1970-2490 nm wavelength, the discrimination rate which was determined by the closest M.D between the sample and the selected spectra was 100% when each spectra was investigated on the same time. But the discrimination rate decreased 50% when the sample and the selected spectra were investigated on the different time. Excluding 1970-2490 nm wavelength, the discrimination rate increased up to 90% when the sample and the selected spectra were investigated on the different time. Comparing the spectra of Korea cigarette brands with those of America cigarette brands by M.D using only 410-1090 nm and 1110-1850 nm wavelength, the spectra of Expo(G) was similar to Winston, Vantage(U.L) and Benson & hedges(M.), the spectra of Hanaro(D) was similar to Carrel, Winston(L), Vantage(U.L), \Vantage and Carrel(L), the spectra of Hanaro(L) was similar to Winston(L) , Carton, Vantage and Carmel(L) and the spectra of Pinetree was similar to Kent, Kool, Kent(G.L), Merit and Benson & hedges(L), respectively. Key Words : NIRS, cigarette brands discrimination