Probabilistic image sensor fusion

Ravi K. Sharma, Todd K. Leen, Misha Pavel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262112450, 9780262112451
Publication statusPublished - 1999
Event12th Annual Conference on Neural Information Processing Systems, NIPS 1998 - Denver, CO, United States
Duration: Nov 30 1998Dec 5 1998


Other12th Annual Conference on Neural Information Processing Systems, NIPS 1998
CountryUnited States
CityDenver, CO


ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Sharma, R. K., Leen, T. K., & Pavel, M. (1999). Probabilistic image sensor fusion. In Advances in Neural Information Processing Systems (pp. 824-830). Neural information processing systems foundation.