Nonuniform image motion estimation using Kalman filtering

This correspondence presents a new pixel-recursive algorithm for estimating the nonuniform image motion from noisy measurements. The proposed method is performed in two steps. First, the pixels are examined to identify the (detectable) moving pixels, using a binary hypothesis testing. Then, characterizing the motion of the identified moving pixels in terms of a unitary transformation, the motion coefficients are estimated using a Kalman filter. Because the motion vector is typically (spatially) slowly varying, the size of the motion coefficient vector is significantly reduced. Consequently, the proposed Kalman filter need only search for the truncated coefficients of the motion field. The proposed method is simulated on a computer, and results are compared with the algorithm reported by Netravali and Robbins (see Bell Syst. Tech. J. vol.58, no.3, p.631-70, Mar. 1979)