A neural network based on differential Gabor filters for computing image flow from two successive images

Proposes a neural network based on differential Gabor filters for computing the image flow. The approach attempts to overcome the limitation of the spatio-temporal frequency models by taking time derivatives of the Gabor responses as the carrier of visual motion information. A differential Gabor filter is a linear filter with the spatial derivative of a Gabo elemental function as its impulse response function. The authors derive a rigorous scheme for computing image motion. Based on this computational scheme, they present the architecture of a neural network system for visual motion. The computational model effectively bypasses the certainty constraint that severely limits the accuracy of the spatio-temporal frequency models, and avoids the time dimension integration required by the spatio-temporal frequency models. Experimental results show that the differential Gabor filter model performs better than the existing models