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. !5
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