Monte Carlo Estimation of Morphological Granulometric Discrete Size Distributions

Morphological granulometries are frequently used as descriptors of granularity, or texture, within a binary image. In this paper, we study the problem of estimating the (discrete) size distribution and size density of a random binary image by means of empirical, as well as, Monte Carlo estimators. Theoretical and experimental results demonstrate superiority of the Monte Carlo estimation approach, and suitability of mathematical morphology in studying important properties of a particular binary random image model, widely known as a Markov random field model.

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