This correspondence deals with the segmentation of a video clip into independently moving visual objects. This is an important step in structuring video data for storage in digital libraries. The method follows a bottom-up approach. The major contribution is a new well-founded measure for motion similarity leading to a robust method for merging regions. The improvements with respect to existing methods have been confirmed by experimental results.

If a camera moves on a straight line, the optic flow field is a diverging vector field, of which the singularity is called "focus of expansion" (FOE). An object which is seen in this FOE is located on the future path of the camera. If the camera is also rotating, the future path is no longer a point in the image domain, but a line. All objects which are on the future path (and thus will cause collisions) are projected on this line. However, the reverse is not necessary true - not all points on the line are collision points. In this paper we derive how the optic flow can be used to compute which points in the image are projections of collision points.

The robot navigation task presented in this paper is to drive through the center of a corridor, based on a sequence of images from an on-board camera. Our measurements of the system state the distance to the wall and orientation of the wall, are derived from the optic flow. Whereas the structure of the environment is usually computed from the spatial derivatives of the optic flow, we used the structure contained in the temporal derivatives of the optic flow to compare the environment structure and hence the system state. The algorithm is used to control a 'remote brain' robot and results on the accuracy of the state estimates are presented.