Non-Bayesian Image Feature Detectors

Ivan Kadar; Erica Liebman; George Eichmann

doi:10.1117/12.939917

11 August 1987 Non-Bayesian Image Feature Detectors

Ivan Kadar, Erica Liebman, George Eichmann

Proceedings Volume 0752, Digital Optical Computing; (1987) https://doi.org/10.1117/12.939917
Event: OE LASE'87 and EO Imaging Symposium, 1987, Los Angeles, CA, United States

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.

Citation Download Citation

Ivan Kadar, Erica Liebman, and George Eichmann "Non-Bayesian Image Feature Detectors", Proc. SPIE 0752, Digital Optical Computing, (11 August 1987); https://doi.org/10.1117/12.939917

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