Estimation of random-model parameters via linear systems with granulometric inputs Conference Paper uri icon

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.

name of conference

  • Mathematical Modeling, Estimation, and Imaging

published proceedings

  • MATHEMATICAL MODELING, ESTIMATION, AND IMAGING

author list (cited authors)

  • Balagurunathan, Y., & Dougherty, E. R.

citation count

  • 0

complete list of authors

  • Balagurunathan, Y||Dougherty, ER

editor list (cited editors)

  • Wilson, D. C., Tagare, H. D., Bookstein, F. L., Preteux, F. J., & Dougherty, E. R.

publication date

  • October 2000