Optical Flow Detection Using a General Noise Model Naoya OHTA

In the usual optical ow detection, the gradient constraint, which expresses the relationship between the gradient of the image intensity and its motion, is combined with the least-squares criterion. This criterion means assuming that only the time derivative of the image intensity contains noise. In this paper, we assume that all image derivatives contain noise and derive a new optical ow detection technique. Since this method requires the knowledge about the covariance matrix of the noise, we also discuss a method for its estimation. Our experiments show that the proposed method can compute optical ow more accurately than the conventional method. key words: image motion, maximum likelihood estimation, least-squares criterion, covariance matrix, generalized eigenvalues, derivative kernels.