منابع مشابه
Adaptive Hausdorff Estimation of Density Level Sets
Hausdorff accurate estimation of density level sets is relevant in applications where a spatially uniform mode of convergence is desired to ensure that the estimated set is close to the target set at all points. The minimax optimal rate of error convergence for the Hausdorff metric is known to be (n/ logn) for level sets with Lipschitz boundaries, where the parameter α characterizes the regular...
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We address the problem of image segmentation with statistical shape priors in the context of the level set framework. Our paper makes two contributions: Firstly, we propose to generate invariance of the shape prior to certain transformations by intrinsic registration of the evolving level set function. In contrast to existing approaches to invariance in the level set framework, this closed-form...
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Preface The following diploma thesis is thought to be a diploma thesis in applied statistics. I declare this in the first paragraph of my work, because you can treat this subject either from a theoretic or an applied view, although the borders between these two areas of statistics cannot be drawn exactly. The reason why I got the idea to treat this subject, is that on the one hand density estim...
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A modification of the kernel estimator for density estimation is proposed which allows the incorporation of local information about the smoothness of the density. The estimator uses a small set of bandwidths rather than a single global one as in the standard kernel estimator. It uses a set of filtering functions which determine the extent of influence of the individual bandwidths. Various versi...
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Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is no...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2006
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2005.05.004