Nonuniform image motion estimation from noisy data

Image motion estimation is viewed as a problem in nonlinear demodulation. The motion D(x) modulates the intensity of the previous frame s(x) to result in the intensity of the present frame s(x-D(x)). An iterative algorithm based on the generalized maximum likelihood criterion is developed and implemented to demodulate D(x). An advantage of this scheme is the incorporation of the covariance function matrix, which is specifically useful in the stationary case to determine the integration region alpha /sub x/. Simulation demonstrated the ability of the algorithm to deal with cases such as Markov-2 motion.>