Showing papers by "Eero P. Simoncelli published in 1993"

Distributed representation and analysis of visual motion

Abstract: 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)

Coarse-to-fine estimation of visual motion

Abstract: The recovery of motion information from visual input is an important task for both natural and artificial vision systems. The standard approach to representing motion information is via the image velocity field: that is, the projection of the motion of points in the three-dimensional world onto the image plane. As an approximation to this, computer vision techniques typically compute an estimate of the motion field from the spatial and temporal variations of image brightness. This field of approximate velocities is known as the “optical flow”.