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Optical Engineering

Bayesian sensor image fusion using local linear generative models
Author(s): Ravi K. Sharma; Todd K. Leen; Misha Pavel
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Paper Abstract

We present a probabilistic method for fusion of images produced by multiple sensors. The approach is based on an image formation model in which the sensor images are noisy, locally linear functions of an underlying true scene (latent variable). A Bayesian framework then provides for maximum-likelihood or maximum a posteriori estimates of the true scene from the sensor images. Least-squares estimates of the parameters of the image formation model involve (local) second-order image statistics, and are related to local principal-component analysis. We demonstrate the efficacy of the method on images from visible-band and infrared sensors.

Paper Details

Date Published: 1 July 2001
PDF: 13 pages
Opt. Eng. 40(7) doi: 10.1117/1.1384886
Published in: Optical Engineering Volume 40, Issue 7
Show Author Affiliations
Ravi K. Sharma, Digimarc Corp. (United States)
Todd K. Leen, Oregon Graduate Institute of Science and Technology (United States)
Misha Pavel, AT&T Labs. (United States)

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