Differential Invariants for Color Images

In this paper, we present a new method for matching points in stereoscopic, uncalibrated color images. Our approach consists first in characterizing points of interest using differential invariants. Then we define additional invariants of first order, exploiting color information. We show that this contribution makes the characterization sufficient till first order. In addition, we make our description robust to usual transformations of image. Second, we present a robust generalization of a gray level corner detector to the case of color images. Third, we propose a simple and efficient scheme for matching these points, using our characterization. Finally, we present matching results and 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 derivatives till first order. 1. Introduction This work addresses the problem of matching stereoscopic, uncalibrated color images. The matching m..