Distributed representation and analysis of visual motion

The central theme of the thesis is that the failure of image motion algorithms is due primarily to the use of vector fields as a representation for visual motion. We argue that the translational vector field representation is inherently impoverished and error-prone. Furthermore, there is evidence that a direct optical flow representation scheme is not used by biological systems for motion analysis. Instead, we advocate distributed representations of motion, in which the encoding of image plane velocity is implicit. As a simple example of this idea, and in consideration of the errors in the flow vectors, we re-cast the traditional optical flow problem as a probabilistic one, modeling the measurement and constraint errors as random variables. The resulting framework produces probability distributions of optical flow, allowing proper handling of the uncertainties inherent in the optical flow computation, and facilitating the combination with information from other sources. We demonstrate the advantages of this probabilistic approach on a set of examples. In order to overcome the temporal aliasing commonly found in time-sampled imagery (eg, video), we develop a probabilistic "coarse-to-fine" algorithm that functions much like a Kalman filter over scale. We implement an efficient version of this algorithm and show its success in computing Gaussian distributions of optical flow of both synthetic and real image sequences. We then extend the notion of distributed representation to a generalized framework that is capable of representing multiple motions at a point. We develop an example representation through a series of modifications of the differential approach to optical flow estimation. We show that this example is capable of representing multiple motions at a single image location and we demonstrate its use near occlusion boundaries and on simple synthetic examples containing transparent objects. Finally, we show that these distributed representation are effective as models for biological motion representation. We show qualitative comparisons of stages of the algorithm with neurons found in mammalian visual systems, suggesting experiments to test the validity of the model. We demonstrate that such a model can account quantitatively for a set of psychophysical data on the perception of moving sinusoidal plaid patterns. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) (Abstract shortened by UMI.)