This paper presents a new 3-D Markov model for motion vector elds. 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 eld. For motion estimation, we use an extended Kalman lter as a pel-recursive estimator. Since a single observation can be sensitive to local image characteristics, especially the model is not accurate, we employ windowed multiple observations at each pixel to increase accuracy. These multiple observations employ di erent weighting values for each observation, since the uncertainty in each observation is di erent. Finally, we compare this 3-D model with earlier proposed 1-D (coarse-to-ne scale) and 2-D spatial compound models, in terms of motion estimation performance on a synthetic and a real image sequence.
Kim and Woods REF presented a 3D Markov model for motion vector fields.
3-D Kalman Filter for Image Motion Estimation
IEEE Trans. Image Processing
Computer Science Mathematics Medicine