Titlebook: Random Sets; Theory and Applicati John Goutsias,Ronald P. S. Mahler,Hung T. Nguyen Book 1997 Springer Science+Business Media New York 1997

annexation

Nikolaos D. Sidiropouloss is a very useful reference work for a broad audience, not limited to the professional lunar scientist: general astronomers, researchers, theoreticians, practitioners, graduate students, undergraduate students, and astrophysicists as well as geologists and engineers. The title includes all current

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Antecedent

0940-6573 96 three-day Summer Program on "Application and Theory of Random Sets." We would like to thank the scientific organizers: John Goutsias (Johns Hopkins University), Ronald P.S. Mahler (Lockheed Martin), and Hung T. Nguyen (New Mexico State University) for their excellent work as organizers of the mee

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Morphological Analysis of Random Sets an Introductionhematical morphology suggest that shapes should be modeled as random closed sets. This approach however is limited by theoretical and practical difficulties. Morphological sampling is used to transform a random closed set into a much simpler discrete random set. It is argued that morphological sampl

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On Estimating Granulometric Discrete Size Distributions of Random Setssuccessfully employed in a number of image processing and analysis tasks, including shape analysis, multiscale shape representation, texture classification, and noise filtering. For most random set models however it is not possible to analytically compute the size distribution. In this contribution,

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Logical Granulometric Filtering in the Signal—Union—Clutter Modelent paper discusses the manner in which filtering can be achieved using morphological granulometric filters. Logical granulometries are unions of intersections of reconstructive openings and these use shape elements to identify image components to be passed (in full), whereas others are deleted. Ass

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On Optimal Filtering of Morphologically Smooth Discrete Random Sets and Related Open Problemss immersed in clutter, or suffering from random dropouts .—.. These results hold for one-sided (union . intersection) noise. In the case of two-sided (union . intersection) noise we now know that this is no longer the case: in this more general setting, as it turns out, optimal estimators are not in

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