Random Sets Theory and Applications

This IMA Volume in Mathematics and its Applications RANDOM SETS: THEORY AND APPLICATIONS is based on the proceedings of a very successful 1996 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 meeting and for editing the proceedings. We also take this opportunity to thank the Army Research Office (ARO), the Office ofNaval Research (0NR), and the Eagan, MinnesotaEngineering Center ofLockheed Martin Tactical Defense Systems, whose financial support made the summer program possible. Avner Friedman Robert Gulliver v PREFACE "Later generations will regard set theory as a disease from which one has recovered. " - Henri Poincare Random set theory was independently conceived by D.G. Kendall and G. Matheron in connection with stochastic geometry. It was however G.

I. Image Modeling and Analysis.- Morphological analysis of random sets. An introduction.- Statistical problems for random sets.- On estimating granulometric discrete size distributions of random sets.- Logical granulometric filtering in the signal-union-clutter model.- On optimal filtering of morphologically smooth discrete random sets and related open problems.- II. Information/Data Fusion and Expert Systems.- On the maximum of conditional entropy for upper/lower probabilities generated by random sets.- Random sets in information fusion. An overview.- Cramér-Rao type bounds for random set problems.- Random sets in data fusion. Multi-object state-estimation as a foundation of data fusion theory.- Extension of relational and conditional event algebra to random sets with applications to data fusion.- Belief functions and random sets.- III. Theoretical Statistics and Expert Systems.- Uncertainty measures, realizations and entropies.- Random sets in decision-making.- Random sets unify, explain, and aid known uncertainty methods in expert systems.- Laws of large numbers for random sets.- Geometric structure of lower probabilities.- Some static and dynamic aspects of robust Bayesian theory.- List of Participants.