In this paper, a motion field representation algorithm based on directional edge information has been developed. This work is aiming at building an ego-motion detection system using dedicated VLSI chips developed for real time motion field generation at low powers . Directional edge maps are utilized instead of original gray-scale images to represent local features of an image and to detect the local motion component in a moving image sequence. Motion detection by edge histogram matching has drastically reduced the computational cost of block matching, while achieving a robust performance of the ego-motion detection system under dynamic illumination variation. Two kinds of feature vectors, the global motion vector and the component distribution vectors, are generated from a motion field at two different scales and perspectives. They are jointly utilized in the hierarchical classification scheme employing multiple-clue matching. As a result, the problems of motion ambiguity as well as motion field distortion caused by camera shaking during video capture have been resolved. The performance of the ego-motion detection system was evaluated under various circumstances, and the effectiveness of this work has been verified.

We propose a method to determine accurate motion fields and illumination conditions such as non-uniform or non-stationary illuminations. The method extends a stabilization method using reliability indices of optical flow to combine with a gradient-based approach that determines a motion field and illumination conditions simultaneously. We applied the proposed method to two synthetic image sequences and a standard image sequence. The method is effective for image sequences including poorly textured areas, edges of brightness variation, and almost dark objects.

A new motion field segmentation algorithm under the 8-parameters motion model is presented which uses a multipass iterative region-refining techinique. The iterative region-refining module consists of a seed block detection and subsequent region-refining iterations. An initial estimate of an object motion is provided in the seed block detection process. This initial estimate is iteratively updated and approaches to a reliable mapping parameter set in region-refining process. A multipass composition of the module makes it possible to detect multiple motions in a scene. Our simulation results confirm that the proposed method successfully partitions an image into independently moving objects with allowable computation time.