• Journal of the Korean Chemical Society
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• v.31 no.6
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• pp.555-566
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• 1987

Fifty ancient Korean coins originated in Yi Dynasty have been determined for 9 elements such as Sn, Fe, As, Ag, Co, Sb, Ir, Ru and Ni by instrumental neutron activation analysis and for 3 elements such as Cu, Pb, and Zn by atomic absorption spectrometry. Bronze coins originated in early days of the dynasty contain as major constituents Cu, Pb and Sn approximately in the ratio 90 : 4 : 3, whereas, those in latter days contain in ratio 7 : 2 : 0. Brass coins which had begun in 17 century contain as major constituents Cu, Zn and Pb approximately in the ratio 7 : 1 : 1. The multivariate data have been analyzed for the relation among elemental contents through the variance-covariance matrix. The data have been further analyzed by a principal component mapping method. As the results training set of 8 class have been chosen, based on the spread of sample points in an eigen vector plot and archaeological data such as age and the office of minting. The training set and test set of samples have finally been analyzed for the assignment to certain classes or outliers through the statistical isolinear multiple component analysis (SIMCA).

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.919-927
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• 2019

In this paper, we propose an accurate camera calibration method for acquiring multiview stereoscopic images. Generally, camera calibration is performed by using checkerboard structured patterns. The checkerboard pattern simplifies feature point extraction process and utilizes previously recognized lattice structure, which results in the accurate estimation of relations between the point on 2-dimensional image and the point on 3-dimensional space. Since estimation accuracy of camera parameters is dependent on feature matching, accurate detection of checkerboard corner is crucial. Therefore, in this paper, we propose the method that performs accurate camera calibration method through accurate detection of checkerboard corners. Proposed method detects checkerboard corner candidates by utilizing 1-dimensional gaussian filters with succeeding corner refinement process to remove outliers from corner candidates and accurately detect checkerboard corners in sub-pixel unit. In order to verify the proposed method, we check reprojection errors and camera location estimation results to confirm camera intrinsic parameters and extrinsic parameters estimation accuracy.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.9-18
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• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.633-640
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• 2006

Spatial clustering, which groups similar objects based on their distance, connectivity, or their relative density in space, is an important component of spatial data mining. Density-based and grid-based clustering are two main clustering approaches. The former is famous for its capability of discovering clusters of various shapes and eliminating noises, while the latter is well known for its high speed. Clustering large data sets has always been a serious challenge for clustering algorithms, because huge data set would make the clustering process extremely costly. In this paper, we propose an enhanced Density-Grid based Clustering algorithm for Large spatial database by setting a default number of intervals and removing the outliers effectively with the help of a proper measurement to identify areas of high density in the input data space. We use a density threshold DT to recognize dense cells before neighbor dense cells are combined to form clusters. When proposed algorithm is performed on large dataset, a proper granularity of each dimension in data space and a density threshold for recognizing dense areas can improve the performance of this algorithm. We combine grid-based and density-based methods together to not only increase the efficiency but also find clusters with arbitrary shape. Synthetic datasets are used for experimental evaluation which shows that proposed method has high performance and accuracy in the experiments.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.6 no.2
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• pp.313-321
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• 1996

Our laboratoy has been participated in Proficiency Analytical Testing (PAT) program which is operated by the Americal Industrial Hygiene Association in cooperation with the National Institute for Occupational Safety and Health (NIOSH). The program is designed to assist a laboratory improve its analytical performance by providing samples on a quarterly basis, evaluating the results, and providing reports on how well the laboratory performed. Evaluation of the results reported here covers five rounds of the PAT program (round 121~round 125). The way a laboratory is evaluated by PAT program is as follows: 1) There is no overall proficiency rating given to a laboratory. 2) A proficiency rating is given for each type of analyze (i.e., metals, silica, asbestos, solvents) that a laboratory analyzed. 3) Proficiency is rated acceptable ("A") if Z score lies between -3 and +3, and unacceptable if Z score is either higher than +3 ("H") or lower than -3 ("Lo"). Z score = (reported data - reference value) / standard deviation 4) For a laboratory to be rated proficient it must either have had no outliers over the most recent two rounds or of the samples actually analyzed over the past year (past four rounds), 75 % or more of the analyze sample results must be acceptable. According to the above rating criteria of PAT program, performance of metals including cadmium, lead, chromium and zinc, and asbestos sample analyses were rated acceptable ("A"). For silica analyses, all samples except one out of four samples in round 122 was rated high("H") were acceptable showing 95 % of ing 95 % of acceptance rate (19/20) throughout the rounds. Analyses of organic solvents were done on 52 samples in 9 types including methanol(MOH), 1,1,1-trichloroethane(MCM), tetrachloroethylene(PCE), trichloroethylene(TCE), benzene(BNZ), o-xylene(OXY), toluene(TOL), chloroform(CFM), 1,2-dichloroethane(DCE). All samples analyzed were rated acceptable except 2 samples that were rated high; one out of each four MCM and TCE samples in round 121, and one that was low out of four o-xylene analyses in round 122 indicating 94 % of acceptance rate(49/52) throughout the rounds. According to the laboratory rating criteria, our laboratory is rated proficient so far for all types of contaminants.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.105-114
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• 2015

This paper proposes methods to remove background area and segment region of interest (ROI) in chest X-ray images. Conventional algorithms to improve detail or contrast of images normally utilize brightness and frequency information. If we apply such algorithms to the entire images, we cannot obtain reliable visual quality due to unnecessary information such as background area. So, we propose two effective algorithms to remove background and segment ROI from the input X-ray images. First, the background removal algorithm analyzes the histogram distribution of the input X-ray image. Next, the initial background is estimated by a proper thresholding on histogram domain, and it is removed. Finally, the body contour or background area is refined by using a popular guided filter. On the other hand, the ROI, i.e., lung segmentation algorithm first determines an initial bounding box using the lung's inherent location information. Next, the main intensity value of the lung is computed by vertical cumulative sum within the initial bounding box. Then, probable outliers are removed by using a specific labeling and the pre-determined background information. Finally, a bounding box including lung is obtained. Simulation results show that the proposed background removal and ROI segmentation algorithms outperform the previous works.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.297-309
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• 2007

The first Korean geostationary weather satellite, Communications, Oceanography and Meteorology Satellite (COMS) will be launched in 2008. The ground station for COMS needs to perform geometric correction to improve accuracy of satellite image data and to broadcast geometrically corrected images to users within 30 minutes after image acquisition. For such a requirement, we developed automated and fast geometric correction techniques. For this, we generated control points automatically by matching images against coastline data and by applying a robust estimation called RANSAC. We used GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline) shoreline database to construct 211 landmark chips. We detected clouds within the images and applied matching to cloud-free sub images. When matching visible channels, we selected sub images located in day-time. We tested the algorithm with GOES-9 images. Control points were generated by matching channel 1 and channel 2 images of GOES against the 211 landmark chips. The RANSAC correctly removed outliers from being selected as control points. The accuracy of sensor models established using the automated control points were in the range of $1{\sim}2$ pixels. Geometric correction was performed and the performance was visually inspected by projecting coastline onto the geometrically corrected images. The total processing time for matching, RANSAC and geometric correction was around 4 minutes.

• Korean Journal of Plant Resources
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• v.28 no.2
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• pp.279-289
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• 2015

Genetic diversity and genetic differentiation of thirteen Pinus densiflora populations in South Korea were estimated using nine ESTP (Expressed Sequence Tag Polymorphism) markers. The numbers of allele and the effective allele were 2.2 and 1.8, respectively. The percentage of polymorphic loci (P) was 98.8%. The observed and the expected heterozygosity were 0.391 and 0.402, respectively, and the eleven populations except for Ahngang and Gangneung population were under Hardy-Weinberg equilibrium state. The level of genetic differentiation (Wright’s F_ST = 0.057) was higher than those of isozyme or nSSR markers. We could not find out any relationship between the genetic distance and geographic distribution among populations from cluster analysis. Also, the genetic differentiation between populations was not correlated with the geographic distance (r = 0.017 and P = 0.344 from Mantel test). From the result of F_ST-outlier analysis to identify a locus under selection, six loci were detected at confidence interval of 99% by the frequentist’s method. However, only three loci (sams2+AluⅠ, sams2+RsaⅠ, PtNCS_p14A9+HaeⅢ) were presumed as outliers by Bayesian method. The sams2+AluⅠ and sams2+RsaⅠlocus were originated from the sams2 gene and seemed to be the loci under balancing selection.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.393-400
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• 2021

Body weight of livestock is a crucial indicator for assessing feed requirements and nutritional status. This study was performed to estimate the body weight of Korean cattle (Hanwoo) using body volume determined from three-dimensional (3-D) image. A TOF camera with a resolution of 640×480 pixels, a frame rate of 44 fps and a field of view of 47°(H)×37°(V) was used to capture the 3-D images for Hanwoo. A grid image of the body was obtained through preprocessing such as separating the body from background and removing outliers from the obtained 3-D image. The body volume was determined by numerical integration using depth information to individual grid. The coefficient of determination for a linear regression model of body weight and body volume for calibration dataset was 0.8725. On the other hand, the coefficient of determination was 0.9083 in a multiple regression model for estimating body weight, in which the age of Hanwoo was added to the body volume as an explanatory variable. Mean absolute percentage error and root mean square error in the multiple regression model to estimate the body weight for validation dataset were 8.2% and 24.5kg, respectively. The performance of the regression model for weight estimation was improved and the effort required for estimating body weight could be reduced as the body volume of Hanwoo was used. From these results obtained, it was concluded that the body volume determined from 3-D of Hanwoo could be used as an effective variable for estimating body weight.