A bayesian formulation for sub-pixel refinement in stereo orbital imagery

Generating accurate three dimensional planetary models is becoming increasingly more important as NASA plans manned missions to return to the moon in the next decade. This paper describes a stereo correspondence system for orbital images and focuses on a novel approach for the sub-pixel refinement of the disparity maps. Our method uses a Bayesian formulation that generalizes the Lucas-Kanade method for optimal matching between stereo pair images. This approach reduces significantly the pixel locking effect of the earlier methods and reduces the influence of image noise. The method is demonstrated on a set of high resolution scanned images from the Apollo era missions.