Normalized phase shift motion energy neuron populations for image velocity estimation

Motion energy neurons are commonly used in biologically motivated algorithms for image velocity estimation. These algorithms typically use a large population of neurons tuned to different locations in the spatial-temporal frequency domain, with each neuron requiring a complex-valued separate spatio-temporal image filter. Here, we show that it is possible to construct a large population of motion energy neurons by combining the outputs of a much fewer number of filters with differing phase shifts. With spatial pooling, the velocity estimation using this phase-tuned population is more reliable than estimation using a more conventional frequency-tuned population. In addition, we show that by normalizing the population response, we can obtain a confidence measure for the resulting velocity estimates.