This paper presents a method for incrementally segmenting images over time using both intensity and motion information. This is done by formulating a model of physically significant image resgions using local constraints on intensity and motion and then finding the optimal segmentation over time using an incremental stochastic minimization technique. The result is a robust and dynamic segmentation of the scene over a sequence of images. The approach has a number of benefits. First, discontinuities are extracted and tracked simultaneously. Second, a segmentation is always available and it improves over time. Finally, by combining motion and intensity, the structural properties of discontinuities can be recovered; that is, discontinuities can be classified as surface markings or actual surface boundaries.
Black, M. J., Combining intensity and motion for incremental segmentation and tracking over long image sequences, in Proc. Second European Conf. on Computer Vision, ECCV-92, G. Sandini (Ed.), Springer Verlag, LNCS 588, May 1992, pp. 485-493. (postscript, 0.14MB)
Black, M. J. and Anandan, P., Dynamic motion estimation and feature extraction over long image sequences, Proc. IJCAI Workshop on Dynamic Scene Understanding, Sydney, Australia, August 1991.