The image formation process involves both geometric and photometric aspects. The definition of optical flow is descriptive and the conformity of optical flow to motion flow is closely related to the algorithm, or the assumption that one takes for optical flow computation. This paper presents an algorithm for optical flow computation based on the mass-conserving assumption by first showing that this assumption is valid under appropriate conditions in both geometric and photometric views. Further discussions on these issues and preliminary experimental results are given.

The image formation process involves both geometric and photometric aspects. The definition of optical flow is descriptive and the conformity of optical flow to motion flow is closely related to the algorithm, or the assumption that one takes for optical flow computation. The paper presents an algorithm for optical flow computation based on the mass-conserving assumption by first showing that this assumption is valid under appropriate conditions in both geometric and photometric views. Further discussions on these issues and preliminary experimental results are given.

We describe a set of image measurements which are invariant to the camera internals but are location variant. We show that using these measurements it is possible to calculate the self-localization of a robot using known landmarks and uncalibrated cameras. We also show that it is possible to compute, using uncalibrated cameras, the Euclidean structure of 3-D world points using multiple views from known positions. We are free to alter the internal parameters of the camera during these operations. Our initial experiments demonstrate the applicability of the method.

Discusses a nonparametric approach for calibrating a CCD camera, a constrained topological mapping (CTM) approach to analyze the systematic imaging errors of an image system and compare it with parametric approaches which are based on optimization and have been discussed by many other authors. This nonparametric approach has several distinct features. In this approach, some distortion surfaces are derived directly from the training samples. Because no analytical form of these surfaces is assumed, when we modeled the distortions by a nonparametric model, the systematic imaging errors instead of mere lens distortions are considered. This gives an new approach to analyze the imaging errors of a particular imaging system. Experimental results are given in detail, which indicate that both in image projection and in 3D reconstruction, the accuracy is much improved when the nonparametric approach is employed for calibrating a camera.>