Depth and Flow From Motion Energy

Proceedings of the AAAI Conference on Artificial Intelligence, 5

Volume

Science

Perception and Robotics

This paper presents a model of motion perception that utilizes the output of motion-sensitive spatiotemporal filters. The power spectrum of a moving texture occupies a tilted plane in the spatiotemporal-frequency domain. The model uses 3-D (space-time) Gabor filters to sample this power spectrum. By combining the outputs of several such filters, the model estimates the velocity of the moving texture - without first computing component (or normal) velocity. A parallel implementation of the model encodes velocity as the peak in a distribution of velocity-sensitive units. For a fixed 3-D rigid-body motion, depth values parameterize a line through image-velocity space. The model estimates depth by finding the peak in the distribution of velocity-sensitive units lying along this line. In this way, depth and velocity are simultaneously extracted.