3-D Kalman filter for image motion estimation
This paper presents a new three-dimensional (3-D) Markov model for motion vector fields. The three dimensions consist of the two space dimensions plus a scale dimension. We use a compound signal model to handle motion discontinuity in this 3-D Markov random field (MRF). For motion estimation, we use an extended Kalman filter as a pel-recursive estimator. Since a single observation can be sensitive to local image characteristics, especially when the model is not accurate, we employ windowed multiple observations at each pixel to increase accuracy. These multiple observations employ different weighting values for each observation, since the uncertainty in each observation is different. Finally, we compare this 3-D model with earlier proposed one-dimensional (1-D) (coarse-to-fine scale) and two-dimensional (2D) spatial compound models, in terms of motion estimation performance on a synthetic and a real image sequence.