Probabilistic Image Sensor Fusion

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 maximuma 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 imagestatistics, and thus are related to local principal component analysis. We demonstrate the e cacy of the method on images from visible-band and infrared sensors.

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: Ravi K. Sharma, Todd K. Leen, Misha Pavel
• Keywords: Image Formation Model, Images, Maximum Likelihood, NIPS 1998, NIPS 2007