This paper is based on the observation that if the viewing camera is appropriately mounted on a vehicle which moves on a planar surface, the problem of motion and structure recovery from optical flow becomes linear and, in principle, can be solved locally. It is shown that angular velocity and depth can be computed from one component only of the optical flow, and that the accuracy in the estimation of depth from the vertical component is more accurate than that from the horizontal component. Experiments on synthetic and real sequences support the presented analysis.>

The accuracy and the dependence on parameters of a general scheme for the analysis of time-varying image sequences are discussed. The approach is able to produce vector fields from which it is possible to recover 3-D motion parameters such as time-to-collision and angular velocity. The numerical stability of the computed optical flow and the dependence of the recovery of 3-D motion parameters on spatial and temporal filtering is investigated. By considering optical flows computed on subsampled images or along single scanlines, it is also possible to recover 3-D motion parameters from reduced optical flows. An adequate estimate of time-to-collision can be obtained from sequences of images with spatial resolution reduced to 128*128 pixels or from sequences of single scanlines passing near the focus of expansion. The use of Kalman filtering increases the accuracy and the robustness of the estimation of motion parameters. The proposed approach seems to be able to provide not only a theoretical background but also practical tools that are adequate for the analysis of time-varying image sequences.>

Two aspects of edge detection are analyzed, namely accuracy of localization and sensitivity to noise. The detection of corners and trihedral vertices is analyzed for gradient schemes and zero-crossing schemes. It is shown that neither scheme correctly detects corners of trihedral vertices, but that the gradient schemes are less sensitive to noise. A simple but important conclusion is that the noise present in digital images of typical indoor scenes is small and the signal-to-noise ratio is high. The noise present in digital images is so small as to make the performances of a variety of filters almost indistinguishable. As a consequence small filters can be used and the exact shape of the filter is not critical.>