Bayesian neural networks and their applications to the classification of stochastic images in stochastic backgrounds

Marek Elbaum; Mark Syrkin

doi:10.1364/OAM.1993.ThGG.2

OSA Annual Meeting
Technical Digest Series (Optica Publishing Group, 1993),
paper ThGG.2
•https://doi.org/10.1364/OAM.1993.ThGG.2

Bayesian neural networks and their applications to the classification of stochastic images in stochastic backgrounds

Marek Elbaum and Mark Syrkin

OSA Annual Meeting 1993

Toronto Canada
3–8 October 1993
ISBN:

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Spatial Vision (ThGG)

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M. Elbaum and M. Syrkin, "Bayesian neural networks and their applications to the classification of stochastic images in stochastic backgrounds," in OSA Annual Meeting, Technical Digest Series (Optica Publishing Group, 1993), paper ThGG.2.

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Abstract

We consider neural network classifiers which can estimate Bayesian a posteriori probabilities¹ as Ideal Observers (IO) for classification of stochastic image patterns for which the Human Observer (HO) is known to be less reliable. Such situations arise across different imaging modalities of interest: medical X- and gamma rays and ultrasound; and passive and active imaging in the visible and the infrared.

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