• Journal of Biomedical Engineering Research
• /
• v.20 no.1
• /
• pp.59-68
• /
• 1999

In this paper, we propose an algorithm to locate the corpus callosum automatically from midsagittal brain MR images using the statistical characteristics and shape information of the corpus callosum. In the proposed algorithm, we first extract regions satisfying the statistical characteristics of the corpus callosum and then find a region matching the shape information. In order to match the shape information, a new directed window region-growing algorithm is proposed instead of using conventional contour matching algorithms. Using the proposed algorithm, we adaptively relax the statistical requirement until we find a region matching the shape information. Experiments show promising results.

• Journal of the Semiconductor & Display Technology
• /
• v.12 no.1
• /
• pp.1-7
• /
• 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
• /
• v.6 no.2
• /
• pp.645-658
• /
• 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.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.11a
• /
• pp.141-146
• /
• 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
• /
• v.12 no.2
• /
• pp.395-411
• /
• 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
• /
• v.29 no.1
• /
• pp.17-27
• /
• 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.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.111-114
• /
• 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.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.706-711
• /
• 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.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.4
• /
• pp.48-57
• /
• 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
• /
• v.17 no.4
• /
• pp.1201-1208
• /
• 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.