Interpretation of optical flow through neural network learning

This study proposes a motion interpretation network which allows optical flow interpretation and describes motions on a plane through the use of a neural network with complex back propagation learning. A network for optical flow normalization is proposed for the interpretation of diverse flow patterns, such as real image optical flow. Using test patterns, the generalization capacity of the proposed network is investigated. The ability is confirmed experimentally