Illumination invariant spatio-colorimetric normalization

In the context of object recognition, it is useful to extract, from the images, efficient indexes that are insensitive to the illumination conditions, to the camera scale factor and to the 2D position and orientation of the object. In this paper, we propose to cope with this invariance problem by normalizing the images according to these parameters in a preprocessing step. This spatio-colorimetric normalization transforms the images so that each pixel get a new position and a new color. These position and color are evaluated according to both the colors and the relative positions of all the pixels in the original image. The comparison of two images is then processed by evaluating the sum of the similarity measures between local indexes extracted at the same positions and scales in the two images. The invariance and the discriminating power of our approach is assessed on a public database.