ESTIMATION OF 3-D MOTION FROM OPTICAL-FLOW WITH UNBIASED OBJECTIVE FUNCTION

This paper describes a noise resistant algorithm for estimating 3-D ri gid motion from optical flow. We first discuss the problem of construc ting the objective function to be minimized. If a Gaussian distributio n is assumed for the noise, it is well-known that the least-squares mi nimization becomes the maximum likelihood estimation. However, the use of this objective function makes the minimization procedure more expe nsive because the program has to go through all the points in the imag e at each iteration. We therefore introduce an objective function that provides unbiased estimators. Using this function reduces computation al costs. Furthermore, since good approximations can be analytically o btained for the function, using them as an initial guess we can apply an iterative minimization method to the function, which is expected to be stable. The effectiveness of this method is demonstrated by comput er simulation.