Reconstructing optical flow generated by camera rotation via autoassociative learning

We investigate methods to reconstruct the optical flow generated by camera rotation using autoassociative learning. A multi-layer perceptron is trained to reduce the dimensionality of flow data which are obtained from real image sequences while the camera is rotating against static scenes. After this learning, the perceptron is able to produce reconstructions of the flow removing the noises in the original flow data. It is also shown that robustness of reconstruction for noisy data is improved by two changes: introduction of confidence values of optical flow into the error function and application of an additional data correction method