Image detection under varying illumination and pose

This paper focuses on the detection of objects with Lambertian surface under both varying illumination and pose. We offer to apply a novel detection method that proceeds by modeling the different illuminations from a small number of images in the training set; this automatically voids the illumination effects, allowing fast illumination invariant detection, without having to create a large training set. It is demonstrated that the method "fits in" nicely with previous work about the modeling of the set of object appearances under varying illumination. In the experiments, an object was correctly detected under image plane rotations in a 15-degrees range, and a wide variety of different illuminations.