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Proceedings Paper

Multistream video fusion using local principal components analysis
Author(s): Ravi K. Sharma; Misha Pavel; Todd K. Leen
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Paper Abstract

We present an approach for fusion of video streams produced by multiple imaging sensors such as visible-band and infrared sensors. Our approach is based on a model in which the sensor images are noisy, locally affine functions of the true scene. This model explicitly incorporates reversals in local contrast, sensor-specific features and noise in the sensing process. Given the parameters of the local affine transformations and the sensor images, a Bayesian framework provides a maximum a posterior estimate of the true scene. This estimate constitutes the rule for fusing the sensor images. We also give a maximum likelihood estimate for the parameters of the local affine transformations. Under Gaussian assumptions on the underlying distributions, estimation of the affine parameters is achieved by local principal component analysis. The sensor noise is estimated by analyzing the sequence of images in each video stream. The analysis of the video streams and the synthesis of the fused stream is performed in a multiresolution pyramid domain.

Paper Details

Date Published: 26 October 1998
PDF: 9 pages
Proc. SPIE 3436, Infrared Technology and Applications XXIV, (26 October 1998); doi: 10.1117/12.327992
Show Author Affiliations
Ravi K. Sharma, Oregon Graduate Institute of Science and Technology (United States)
Misha Pavel, Oregon Graduate Institute of Science and Technology (United States)
Todd K. Leen, Oregon Graduate Institute of Science and Technology (United States)

Published in SPIE Proceedings Vol. 3436:
Infrared Technology and Applications XXIV
Bjorn F. Andresen; Marija Strojnik, Editor(s)

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