Stochastic representation of memoryless Boolean functions: application to boundary estimation at low contrast

Author

Roysam, Badrinath ; Miller, Michael

Author_Institution

Dept. of Electr., Comput., & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY, USA

fYear

1990

fDate

3-6 Apr 1990

Firstpage

2333

Abstract

Earlier work on parallel computation of generalized Bayesian hypothesis tests for hierarchical image reconstruction on massively parallel processor arrays is extended to incorporate pattern constraints specified with Boolean functions defined on symbolic imaging variables. This is based on a stochastic representation for memoryless Boolean functions following U. Grenander´s work (1984) on metric pattern theory. Its application is presented to the segmentation of low-contrast textured images through an extension of J. Besag´s (1986) ICM segmentation algorithm, and to image reconstruction with large point spread. Hierarchical image reconstruction in time-of-flight positron emission tomography at low count levels is described

Keywords

Bayes methods; Boolean functions; computerised picture processing; computerised tomography; parallel algorithms; ICM segmentation algorithm; boundary estimation; generalized Bayesian hypothesis tests; hierarchical image reconstruction; large point spread; low-contrast textured images; memoryless Boolean functions; parallel computation; pattern constraints; segmentation; stochastic representation; symbolic imaging variables; time-of-flight positron emission tomography; Bayesian methods; Boolean functions; Concurrent computing; Differential equations; Image reconstruction; Image segmentation; Logic testing; Maximum likelihood estimation; Positron emission tomography; Stochastic processes; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.116050

Filename

116050