1 May 1994 Maximum-likelihood estimation in the discrete random Boolean model
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Edward R. Dougherty, Edward R. Dougherty, John C. Handley, John C. Handley, } "Maximum-likelihood estimation in the discrete random Boolean model", Proc. SPIE 2180, Nonlinear Image Processing V, (1 May 1994); doi: 10.1117/12.172553; https://doi.org/10.1117/12.172553

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