Autocalibration is a difficult problem. Not only is its computation very noisesensitive, but there also exist many critical motions that prevent the estimation of some of the camera parameters. When a ?stratified? approach is considered, affine and Euclidean calibration are computed in separate steps and it is possible to see that a part of these ambiguities occur during affine-to- Euclidean calibration. This paper studies the affine-to-Euclidean step in detail using the real Jordan decomposition of the infinite homography. It gives a new way to compute the autocalibration and analyzes the effects of critical motions on the computation of internal parameters. Finally, it shows that in some cases, it is possible to obtain complete calibration in the presence of critical motions.