Accession Number:

ADA091044

Title:

Fitting Random Field Models to Images.

Descriptive Note:

Interim rept.,

Corporate Author:

MARYLAND UNIV COLLEGE PARK COMPUTER SCIENCE CENTER

Personal Author(s):

Report Date:

1980-08-01

Pagination or Media Count:

43.0

Abstract:

This paper deals with fitting two-dimensional stationary random field RF models to images. We assume that the given image is represented on a torus lattice, obeying an R.F. model driven by uncorrelated noise. The stochastic model is characterized by a set of unknown parameters. We describe two sets of experimental results. First, by assigning values to parameters in the stationary range, two-dimensional patterns are generated. It appears that quite a variety of patterns can be generated. Next we consider the problem of estimating the parameters, given an arbitrary image. By assuming a Gaussian structure for the noise, we given an iterative scheme to estimate the unknown parameters. We also implement a decision rule to choose an appropriate set of neighbors for the image. The theory is illustrated by applying it to synthetic patterns. Author

Subject Categories:

  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE