Algebraic analysis of the generating functional for discrete random sets and statistical inference for intensity in the discrete Boolean random-set model

N. D. Sidiropoulos, J. S. Baras, C. A. Berenstein

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

We consider binary digital images as realizations of a uniformly bounded discrete random set, a mathematical object that can be defined directly on a finite lattice. In this setting we show that it is possible to move between two equivalent probabilistic model specifications. We formulate a restricted version of the discrete-case analog of a Boolean random-set model, obtain its probability mass function, and use some methods of morphological image analysis to derive tools for its statistical inference.

Original languageEnglish (US)
Pages (from-to)273-290
Number of pages18
JournalJournal of Mathematical Imaging and Vision
Volume4
Issue number3
DOIs
StatePublished - Jul 1 1994

Keywords

  • Boolean model and random sets
  • decision theory
  • image modeling and analysis
  • mathematical morphology
  • morphological skeleton

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