• Corpus ID: 5447932

Probabilistic Image Sensor Fusion

@inproceedings{Sharma1998ProbabilisticIS,
  title={Probabilistic Image Sensor Fusion},
  author={Ravi K. Sharma and Todd K. Leen and Misha Pavel},
  booktitle={NIPS},
  year={1998}
}
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… 

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References

SHOWING 1-10 OF 12 REFERENCES

Model-based sensor fusion for aviation

A sensor fusion algorithm based on a set of simple assumptions about the relationship among the sensors is described, and the optimal fusion is then approximated by a weighted sum of the common component in each sensor output at each pixel.

Enhanced image capture through fusion

The authors present an extension to the pyramid approach to image fusion that provides greater shift invariance and immunity to video noise, and provides at least a partial solution to the problem of combining components that have roughly equal salience but opposite contrasts.

Automatic visual/IR image registration

A feature-based approach to visual/IR sensor image registration overcomes the difficulties caused by the discrepancy in data’s gray-scale characteristics and the problem of feature inconsistency by employing a wavelet-based feature extractor.

Probabilistic Principal Component Analysis

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based on a probability model. We demonstrate how the principal axes of a set of

Data Fusion for Sensory Information Processing Systems

The role of data Fusion in Sensory Systems, Bayesian Sensory Information Processing, and Temporal Aspects of Data Fusion are introduced.

Sensor and Data Fusion Concepts and Applications

Multiple Sensor System Applications, Benefits, and Atmospheric Attenuation Data Fusion Algorithms and Architectures Bayesian Inference Dempster-Shafer Algorithm Artificial Neural Networks Voting

Image processing for flight crew enhanced situation awareness

The issues involved in each category of processing, the most promising algorithms are described, and preliminary results of the image processing are presented.

Infrared-optical multisensor for autonomous landing guidance

Infrared sensors at the nominal 8 - 12 and 3 - 5 micron wavebands respectively can be shown to have complementary performance characteristics when used over a range of meteorological conditions. The

Statistical Factor Analysis and Related Methods

Sensor fusion for synthetic vision

Display methodologies are explored for fusing images gathered by millimeter wave sensors with images rendered from an on-board terrain data base to facilitate visually guided flight and ground