• Communications for Statistical Applications and Methods
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• v.12 no.1
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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.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.25-32
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• 2004

We propose a hierarchical disparity estimation algorithm with edge-preserving energy-based regularization. Initial disparity vectors are obtained from downsampled stereo images using a feature-based region-dividing disparity estimation technique. Dense disparities are estimated from these initial vectors with shape-adaptive windows in full resolution images. Finally, the vector fields are regularized with the minimization of the energy functional which considers both fidelity and smoothness of the fields. The first two steps provide highly reliable disparity vectors, so that local minimum problem can be avoided in regularization step. The proposed algorithm generates accurate disparity map which is smooth inside objects while preserving its discontinuities in boundaries. Experimental results are presented to illustrate the capabilities of the proposed disparity estimation technique.

• Journal of the Korean Statistical Society
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• v.30 no.1
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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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• v.24 no.2
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.4 s.175
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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.