A neural computational scheme for extracting optical flow from the Gabor phase differences of successive images

The authors propose a neurobiologically plausible representation of the Gabor phase information, and present a neural computation scheme for extracting visual motion information from the Gabor phase information. The scheme can compute visual motion accurately from a scene with illumination changes, while other neural schemes for optical flow must assume stable brightness. The computational tests on synthetic and natural image data showed that the scheme was robust to the natural scenes. An architecture is presented of a neural network system based on the Gabor phase representation of visual motion