Share Email Print

Proceedings Paper

Non-Bayesian Image Feature Detectors
Author(s): Ivan Kadar; Erica Liebman; George Eichmann
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

In this paper non-Bayesian and heuristic approaches are applied to the well known problem of image segmentation. The two subproblems in segmentation that were considered were region merge and line detection. For the region merge problem, a comparison was made between the classical Bayes and fuzzy set based approach. Simulations, using a "block world" type real image, were implemented in ZETALISP on the Symbolics 3675 computer. They contrasted the proposed region merge method with the classical implementations. The performance measures of the classical line detection problem, using the Hough transform, are reinterpreted in a non-traditional framework using fuzzy sets and heuristics. Several alternative real-time optical Hough transform schemes are presented as well.

Paper Details

Date Published: 11 August 1987
PDF: 8 pages
Proc. SPIE 0752, Digital Optical Computing, (11 August 1987); doi: 10.1117/12.939917
Show Author Affiliations
Ivan Kadar, Grumman Corporation (United States)
Erica Liebman, Grumman Corporation (United States)
George Eichmann, The City College of CUNY (United States)

Published in SPIE Proceedings Vol. 0752:
Digital Optical Computing
Raymond Arrathoon, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?