Probabilistic Image Sensor Fusion
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. A Bayesian framework then provides for maximum likelihood or maximum a posteriori estimates of the true scene from the sensor images. Maximum likelihood estimates of the parameters of the image formation model involve (local) second order image statistics, and thus are related to local principal component analysis. We demonstrate the efficacy of the method on images from visible-band and infrared sensors.
Cite
Text
Sharma et al. "Probabilistic Image Sensor Fusion." Neural Information Processing Systems, 1998.Markdown
[Sharma et al. "Probabilistic Image Sensor Fusion." Neural Information Processing Systems, 1998.](https://mlanthology.org/neurips/1998/sharma1998neurips-probabilistic/)BibTeX
@inproceedings{sharma1998neurips-probabilistic,
title = {{Probabilistic Image Sensor Fusion}},
author = {Sharma, Ravi K. and Leen, Todd K. and Pavel, Misha},
booktitle = {Neural Information Processing Systems},
year = {1998},
pages = {824-830},
url = {https://mlanthology.org/neurips/1998/sharma1998neurips-probabilistic/}
}