4 October 2000 Estimation of random model parameters via linear systems with granulometric inputs
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Morphological granulometries have been used to successfully discriminate textures in the context of classical feature-based classification. The features are typically the granulometric moments resulting from the pattern spectrum of the random image. This paper takes a different approach and uses the granulometric moments as inputs to a linear system that has been derived by classical optimization techniques for linear filters. The output of the system in a set of estimators that estimate the parameters of the model governing the distribution of the random set. These model parameters are assumed to be random variables possessing a prior distribution, so that the linear filter estimates these random variables based on granulometric moments. The methodology is applied to estimating the primary grain and intensity of a random Boolean model.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yoganand Balagurunathan, Yoganand Balagurunathan, Edward R. Dougherty, Edward R. Dougherty, "Estimation of random model parameters via linear systems with granulometric inputs", Proc. SPIE 4121, Mathematical Modeling, Estimation, and Imaging, (4 October 2000); doi: 10.1117/12.402446; https://doi.org/10.1117/12.402446


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