Maximum-likelihood estimation in the discrete random Boolean model

Edward R. Dougherty; John C. Handley

doi:10.1117/12.172553

1 May 1994 Maximum-likelihood estimation in the discrete random Boolean model

Proceedings Volume 2180, Nonlinear Image Processing V; (1994) https://doi.org/10.1117/12.172553
Event: IS&T/SPIE 1994 International Symposium on Electronic Imaging: Science and Technology, 1994, San Jose, CA, United States

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.

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Edward R. Dougherty and John C. Handley "Maximum-likelihood estimation in the discrete random Boolean model", Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); https://doi.org/10.1117/12.172553

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