Probabilistic invariant image representation and associated distance measure

Varying illumination is a limiting factor for many computer vision applications, especially in outdoor settings. Invariant image representations aim to reduce this effect and provide the following processing steps, image segmentation, edge detection, object recognition, etc., with a more stable view, closer to the surface reflectances presents in the scene than to the illumination. In this work we present an invariant image representation that integrates several key observations in a probabilistic way and an associated probabilistic distance measure. They can be used as a measure of similarity between the surfaces represented by a given pair of pixels, even under illumination color changes.