• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.149-167
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• 2005

The minimum disparity estimators introduced by Lindsay and Basu (1994) are studied empirically. An extensive simulation in this paper provides a location estimate of the small sample and supplies empirical evidence of the estimator performance for the univariate contaminated normal model. Empirical results show that the minimum generalized negative exponential disparity estimator (MGNEDE) obtains high efficiency for small sample sizes and dominates the maximum likelihood estimator (MLE) and the minimum blended weight Hellinger distance estimator (MBWHDE) with respect to efficiency at the contaminated model.

• 대한전자공학회논문지SP
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• 제41권6호
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• pp.25-32
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• 2004

본 논문에서는 스테레오 영상으로부터 자연스러우면서도 정확한 변이 정보를 추출하기 위한 변이 추정 알고리듬을 제안한다. 제안된 알고리듬은 영역 분할 기법을 이용한 계층적 변이 추정부와 편미분 방정식(PDE: Partial Differential Equation)을 이용한 에너지 기반 경계 보존 변이 평활화부로 구성되어 있다. 제안된 계층적 변이 추정 기법은 빠르면서도 신뢰도 있는 변이를 제공하며, 이러한 변이장은 정확도와 평활화도를 함께 고려한 에너지 모델의 최소화 기법에 의해 자연스럽고 정밀한 최종 변이장으로 추출된다. 에너지 모델의 최소화 과정은 대응되는 Euler-Lagrange 방정식으로 변형되어 유한차분법(FDM: Finite difference Method)을 이용한 근사화를 통해 구현된다. 실험을 통해 제안된 변이 추정 기법은 다양한 환경의 영상에 대해서도 자연스러우면서도 정확하고, 경계가 잘 보존된 변이를 추정해 낼 수 있음을 검증하였다.

• Journal of the Korean Statistical Society
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• 제30권1호
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• pp.41-61
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• 2001

The minimum negative exponential disparity estimator(MNEDE), introduced by Lindsay(1994), is an excellenet competitor to the minimum Hellinger distance estimator(Beran 1977) as a robust and yet efficient alternative to the maximum likelihood estimator in parametric models. In this paper we define the negative exponential deviance test(NEDT) as an analog of the likelihood ratio test(LRT), and show that the NEDT is asymptotically equivalent to he LRT at the model and under a sequence of contiguous alternatives. We establish that the asymptotic strong breakdown point for a class of minimum disparity estimators, containing the MNEDE, is at least 1/2 in continuous models. This result leads us to anticipate robustness of the NEDT under data contamination, and we demonstrate it empirically. In fact, in the simulation settings considered here the empirical level of the NEDT show more stability than the Hellinger deviance test(Simpson 1989). The NEDT is illustrated through an example data set. We also define a goodness-of-fit statistic to assess adequacy of a specified parametric model, and establish its asymptotic normality under the null hypothesis.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

• 대한기계학회논문집A
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• 제24권4호
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• pp.991-1000
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• 2000

Previous area-based stereo matching algorithms find the disparity by first computing the sum of squared differences (SSD) between corresponding points using a rectangular window, and then searching the position of the minimum SSD within the disparity range. These algorithms generate relatively many matching errors around depth discontinuities, since the SSD function may fail to search for the minimum because of varying disparity profiles in such areas. In this paper, in order to improve the matching accuracy around the depth discontinuities, a new correlation function based on robust estimation technique is proposed for stereo matching. In addition, while previous stereo algorithms utilize a single rectangular window for computing the correlation function, the proposed matching algorithm utilizes 4-directional line masks additionally to reduce the matching errors further. It has been turned out that the proposed algorithm reduces matching errors around depth discontinuities significantly. Experimental results are presented in this paper, comparing the performance of the proposed technique with those of previous algorithms using both synthetic and real images.