• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.484-487
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• 2003

A new domain PCA-based approach to watermarking is presented. This method is applied to digital images to embed and detect a watermark. The performance of PCA approach is compared to traditional frequency domain watermark models. Simulation shows the performance of the proposed method with excellent result against image cropping and robustness against some attacks such as additive noise, filtering and jpeg compression.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.232-234
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• 2002

EEG-based brain computer interface (BCI) provides a new communication channel between human brain and computer. The classification of EEG data is an important task in EEG-based BCI. In this paper we present methods which jointly employ principal component analysis (PCA) and linear dynamical system (LDS) modeling for the task of EEG classification. Experimental study for the classification of EEG data during imagination of a left or right hand movement confirms the validity of our proposed methods.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.41-48
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• 2005

This paper presents a face recognition method based on the combination of well-known statistical representations of Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA) with Radial Basis Function Networks. The original face image is first processed by PCA to reduce the dimension, and thereby avoid the singularity of the within-class scatter matrix in LDA calculation. The result of PCA process is applied to LDA classifier. In the second approach, the LDA process Produce a discriminational features of the face image, which is taken as the input of the Radial Basis Function Network(RBFN). The proposed approaches has been tested on the ORL face database. The experimental results have been demonstrated, and the recognition rate of more than 93.5% has been achieved.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.1111-1116
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• 2009

In this paper, we discuss distortion-tolerant pattern recognition using computational integral imaging reconstruction. Three-dimensional object information is captured by the integral imaging pick-up process. The captured information is numerically reconstructed at arbitrary depth-levels by averaging the corresponding pixels. We apply Fisher linear discriminant analysis combined with principal component analysis to computationally reconstructed images for the distortion-tolerant recognition. Fisher linear discriminant analysis maximizes the discrimination capability between classes and principal component analysis reduces the dimensionality with the minimum mean squared errors between the original and the restored images. The presented methods provide the promising results for the classification of out-of-plane rotated objects.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1945-1953
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• 2022

Korea Aerospace Research Institute produces mosaic images of the Korean Peninsula every year to promote the use of satellite images and provides them to users in the public sector. However, since the pan-sharpening and color balancing methodologies are applied during the mosaic image processing, the original spectral information is distorted. In addition, there is a limit to analyze using mosaic images as mosaic images provide only Red, Green and Blue bands excluding Near Infrared (NIR) band. Therefore, in order to compensate for these limitations, this study applied the Principal Component Analysis (PCA) technique and indices extracted from R, G, B bands together for image classification and compared the classification results. As a result of the analysis, the accuracy of the mosaic image classification result was about 67.51%, while the accuracy of the image classification result using both PCA and RGB indices was about 75.86%, confirming that the accuracy of the image classification result can be improved. As a result of comparing the PCA and the RGB indices, the accuracy of the image classification result was about 64.10% and 74.05% respectively. Through this, it was confirmed that the classification accuracy using the RGB indices was higher among the two techniques, and implications were derived that it was important to use high quality reference or supplementary data. In the future, additional indices and techniques are needed to improve the classification and analysis results of mosaic images, and related research is expected to increase the utilization of images that provide only R, G, B or limited spectral information.

• Applied Science and Convergence Technology
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• v.23 no.6
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• pp.328-339
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• 2014

In this article, plasma monitoring tools and mulivariate analysis techniques were reviewed. Optical emission spectroscopy was reviewed for a chemical composition analysis tool and RF V-I probe for a physical analysis tool for plasma monitoring. Multivariate analysis techniques are discussed to the sensitivity improvement. Principal component analysis (PCA) is one of the widely adopted multivariate analysis techniques and its application to end-point detection of plasma etching process is discussed.

• Bulletin of the Korean Chemical Society
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• v.7 no.1
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• pp.73-77
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• 1986

Data on the concentration of Na, K, Sc, Cr, Fe, Co, Cu, Ga, Rb, Cs, Ba, La, Ce, Sm, Eu, Tb, Lu, Hf, Ta, and Th obtained by neutron activation analysis have been used to characterize Korean porcelainsherds by multivariate analysis. The mathematical approach employed is principal component analysis (PCA). PCA was found to be helpful for dimensionality reduction and for obtaining information regarding (a) the number of independent causal variables required to account for the variability in the overall data set, (b) the extent to which a given variable contributes to a component and (c) the number of causal variables required to explain the total variability of each measured variable.

• Journal of Korean Society for Atmospheric Environment
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• v.8 no.3
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• pp.179-190
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• 1992

This study was carried out to characterize sources of atmospheric total suspended particulates (TSP) in urban and sub--urban areas of metropolitan taegu. The sources were tentatively identified by a multivariate technique, i.e. principal component analysis (PCA), and the source contributions to the atmospheric concentrations of TSP were further estimated by stepwise multiple regression analysis. A total of 5 sources was identified in the urban area of Taegu (soil dust resuspension, fuel combustion, secondary aerosol, traffic related aerosol, and refuge burning), while 4 sources were found to be significant in the sub--urban area as following: fuel combustion/secondary aerosol, soil dust resuspension, traffic related aerosol, and wood/agricultural burning. The largest contributor to the atmospheric TSP appeared to be the soil dust resuspension in both areas. The source apportionment of the extractable organic matter (EOM) was also carried out for the Taegu data. The EOM was determined with respect to the solvent polarity, i.e. cyclohexane (non-polar), dichloromethane (semi--polar), and acetone (polar). In addition, the source profiles for the TSP in Taegu area were estimated using a PCA-based algorithm, and the validity was evaluated tentatively by comparing the data in the literature.

• Journal of Korea Multimedia Society
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• v.18 no.6
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• pp.701-709
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• 2015

Image stitching method is a technique for obtaining an high-resolution image by combining two or more images. In X-ray image for clinical diagnosis, the size of the imaging region taken by one shot is limited due to the field-of-view of the equipment. Therefore, in order to obtain a high-resolution image including large regions such as a whole body, the synthesis of multiple X-ray images is required. In this paper, we propose a rapid stitching method of digital X-ray images using template-based registration. The proposed algorithm use principal component analysis(PCA) and k-nearest neighborhood(k-NN) to determine the location of input images before performing a template-based matching. After detecting the overlapping position using template-based matching, we synthesize input images by alpha blending. To improve the computational efficiency, reduced images are used for PCA and k-NN analysis. Experimental results showed that our method was more accurate comparing with the previous method with the improvement of the registration speed. Our stitching method could be usefully applied into the stitching of 2D or 3D multiple images.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.984-990
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• 2013

Pick and placement systems need high speeds and reliability for the position alignment process of semiconductor packages in picking up and placing them on placement trays. Image processing is usually adopted for position aligning where finding out the most suitable method is considered most important aspect of the process. This paper proposes a method for judging the performance of different image processing algorithms based on the PCI (Process Capability Index). The PCI is an index which represents the error distribution acquired from many experimental data. The bigger the index, the more reliable the results or the lower the deviation. Two compared and candidate methods are Hough Transform and PCA (Principal Component Analysis), both of which are very suitable for oblong or rectangular type packages such as BGA's. Comparing the two approaches through a CPI with enough experimental results leads to the conclusion that the PCA is much better than the Hough Transform in not only reliability, but also processing speed.