Differential invariants for color images

We present a new method for matching points in stereoscopic, uncalibrated color images. Our approach consists of characterizing points of interest using differential invariants. We define additional invariants of first order, exploiting color information. We show that this contribution makes the characterization sufficient for first order. In addition, we make our description robust to usual transformations of image. We present a robust generalization of a gray level corner detector to the case of color images. We also propose a simple and efficient scheme for matching these points, using our characterization. Finally, we present matching results and the epipolar geometry obtained on complex scenes, which clearly show the pertinence of our approach. We are able to match points robustly and rapidly, using only first order derivatives.