This paper focuses on the detection of objects with a Lambertian surface under 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 a 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 modeling the set of object appearances under varying illumination. In the experiments, an object was correctly detected under image plane rotations in a 45° range, and a wide variety of different illuminations, even when significant shadows were present.
- Varying illumination
- Varying pose
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition