TY - CONF
T1 - Statistical biases in optic flow
T2 - Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Y1 - 1999
A1 - FermÃ¼ller, Cornelia
A1 - Pless, R.
A1 - Aloimonos, J.
KW - Distributed computing
KW - Frequency domain analysis
KW - HUMANS
KW - image derivatives
KW - Image motion analysis
KW - Image sequences
KW - Least squares methods
KW - Motion estimation
KW - Optical computing
KW - Optical distortion
KW - optical flow
KW - Optical noise
KW - Ouchi illusion
KW - perception of motion
KW - Psychology
KW - Spatiotemporal phenomena
KW - statistical analysis
KW - systematic bias
KW - total least squares
AB - 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
JA - Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
PB - IEEE
VL - 1
SN - 0-7695-0149-4
M3 - 10.1109/CVPR.1999.786994
ER -