%0 Conference Paper
%B Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
%D 1999
%T Statistical biases in optic flow
%A FermÃ¼ller, Cornelia
%A Pless, R.
%A Aloimonos, J.
%K Distributed computing
%K Frequency domain analysis
%K HUMANS
%K image derivatives
%K Image motion analysis
%K Image sequences
%K Least squares methods
%K Motion estimation
%K Optical computing
%K Optical distortion
%K optical flow
%K Optical noise
%K Ouchi illusion
%K perception of motion
%K Psychology
%K Spatiotemporal phenomena
%K statistical analysis
%K systematic bias
%K total least squares
%X The computation of optical flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing optic flow from image derivatives even in regions of consistent flow can lead to a systematic bias dependent upon the direction of the optic flow, 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 flow in the spatiotemporal frequency domain. The predicted bias in the optical flow 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 difficulty of the task and the feasibility of using this distorted optic flow or undistorted normal flow in tasks requiring higher lever processing
%B Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
%I IEEE
%V 1
%P 566 Vol. 1 - 566 Vol. 1
%8 1999///
%@ 0-7695-0149-4
%G eng
%R 10.1109/CVPR.1999.786994