Statistical Biases in Optic Flow

The computation of optical ow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic ow from image derivatives even in regions of consistent ow can lead to a systematic bias dependent upon the direction of the optic ow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing optical ow in the spatiotemporal frequency domain. The predicted bias in the optical ow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the diiculty of the task and the feasibility of using this distorted optic ow or undistorted normal ow in tasks requiring higher lever processing.