• 대한의용생체공학회:의공학회지
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• 제20권1호
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• pp.59-68
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• 1999

본 논문에서는 뇌량의 형태정보와 통계적 특성을 이용한 중앙시상 두뇌자기공명영상의 뇌량자동인식 알고리즘을 제안한다. 제안된 알고리즘에서는 우선 뇌량의 통계적 특성에 일치하는 영역들을 추출하고 형태정보와 일치하는 영역을 검출한다. 이러한 형태정합을 위해 기존의 윤곽정합알고리즘 대신에 통계적인 특성을 적응적으로 변화시켜 형태정보와 일치하는 영역을 검출하는 방향성 창영역확장 알고리즘을 제안하였다. 실험결과 제안된 알고리즘의 우수성을 확인할 수 있었다.

• 반도체디스플레이기술학회지
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• 제12권1호
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.645-658
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• 1999

One of the most popular graphical techniques for goodness of fit problems is the quantile-quantile plot(Q-Q plot) Easton and McCulloch(1990) suggested a way of generalizing Q-Q plots to multivariate cases bases on finding a matching between the points of the data set whose shape is being examined and a reference sample. in this paper we investigated the asymptotic behavior of the generalized Q-Q plot for bivariate cases. As a result we concluded that the standard univariate Q-Q plot and the generalized Q-Q plot have the same limit if two variables are independent.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2005년도 추계 학술발표회 논문집
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• pp.141-146
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• 2005

Ensemble method has been known as one of the most powerful classification tools that can improve prediction accuracy. Ensemble method also has been understood as ‘perturb and combine’ strategy. Many studies have tried to develop ensemble methods by improving perturbation. In this paper, we propose two new ensemble methods that improve combining, based on the idea of pattern matching. In the experiment with simulation data and with real dataset, the proposed ensemble methods peformed better than bagging. The proposed ensemble methods give the most accurate prediction when the pruned tree was used as the base learner.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.395-411
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• 2005

In this paper, we develop the Jeffreys' prior, reference prior and the the probability matching priors for the difference of intraclass correlation coefficients in familial data. e prove the sufficient condition for propriety of posterior distributions. Using marginal posterior distributions under those noninformative priors, we compare posterior quantiles and frequentist coverage probability.

• Journal of the Korean Statistical Society
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• 제29권1호
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• pp.17-27
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• 2000

In this paper, we develop noninformative priors that are used for estimating the reliability of stress-strength system under the Burr-type X model. A class of priors is found by matching the coverage probabilities of one-sided Bayesian credible interval with the corresponding frequentist coverage probabilities. It turns out that the reference prior as well as the Jeffreys prior are the second order matching prior. The propriety of posterior under the noninformative priors is proved. The frequentist coverage probabilities are investigated for samll samples via simulation study.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2008년도 International Symposium on Remote Sensing
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• pp.111-114
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• 2008

The existence of noise bands may deform the typical shape of the spectrum, making the accuracy of clustering degraded. This paper proposes a statistical approach to remove noise bands in hyperspectral data using the correlation coefficient of bands as an indicator. Considering each band as a random variable, two adjacent signal bands in hyperspectral data are highly correlative. On the contrary, existence of a noise band will produce a low correlation. For clustering, the unsupervised ${\kappa}$-nearest neighbor clustering method is implemented in accordance with three well-accepted spectral matching measures, namely ED, SAM and SID. Furthermore, this paper proposes a hierarchical scheme of combining those measures. Finally, a separability assessment based on the between-class and the within-class scatter matrices is followed to evaluate the applicability of the proposed noise band removal method. Also, the paper brings out a comparison for spectral matching measures.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집B
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• pp.706-711
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• 2001

This paper represents a relaxation matching algorithm based on global structure constraint satisfaction. Relaxation matching algorithm is a conventional approach to the matching problem. However, we confronted some problems such as null-matching and multi-matching problems by just using the relaxation matching technique. In order to solve the problems, in this paper, the matching problem is regarded as constraint satisfaction problem, and a relaxation matching algorithm is proposed based on global structure constraint satisfaction. The proposed algorithm is applied a landslide picture to show the effectiveness. When the algorithm is processed at landslide inspecting and monitoring system, motion parameters such as displacement area and its direction are computed. Once movement is recognized, displacements are estimated graphically with statistical amount in the image plane. Simulation has been done to prove the proposed algorithm by using time-sequence image of landslide inspection and monitoring system.

• 전자공학회논문지B
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• 제29B권4호
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• pp.48-57
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• 1992

This paper addresses the estimation of the shift vector from aerial image sequences. The conventional feature-based and area-based matching methods are simulated for determining the suitable image registration scheme. Computer simulations show that the feature-based matching schemes based on the co-occurrence matrix, autoregressive model, and edge information do not give a reliable matching for aerial image sequences which do not have a suitable statistical model or significant features. In area-based matching methods we try various similarity functions for a matching measure and discuss the factors determining the matching accuracy. To reduce the estimation error of the shift vector we propose the reference window selection scheme. We also discuss the performance of the proposed algorithm based on the simulation results.

• Journal of the Korean Data and Information Science Society
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• 제17권4호
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• pp.1201-1208
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• 2006

Data fusion is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences. Data fusion is also called data combination or data matching. Data fusion is divided in five branch types which are exact matching, judgemental matching, probability matching, statistical matching, and data linking. Currently, Gyeongnam province is executing the social survey every year with the provincials. But, they have the limit of the analysis as execute the different survey to 3 year cycles. In this paper, we study to data fusion of environmental survey data using sas macro. We can use data fusion outputs in environmental preservation and environmental improvement.