Estimation of random-model parameters via linear systems with granulometric inputs
Conference Paper
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
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 is 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.