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Proceedings Paper

Maximum-likelihood estimation in the discrete random Boolean model
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Paper Abstract

The exact probability density for a windowed observation of a discrete 1D Boolean process having convex grains is found via recursive probability expressions. This observation density is used as the likelihood function for the process and numerically yields the maximum- likelihood estimator for the process intensity and the parameters governing the distribution of the grain lengths. Maximum-likelihood estimation is applied in the case of Poisson distributed lengths.

Paper Details

Date Published: 1 May 1994
PDF: 8 pages
Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); doi: 10.1117/12.172553
Show Author Affiliations
Edward R. Dougherty, Rochester Institute of Technology (United States)
John C. Handley, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2180:
Nonlinear Image Processing V
Edward R. Dougherty; Jaakko Astola; Harold G. Longbotham, Editor(s)

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