3-D Kalman filter for image motion estimation
- 1 January 1998
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 7 (1) , 42-52
- https://doi.org/10.1109/83.650849
Abstract
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.Keywords
This publication has 22 references indexed in Scilit:
- A new interpretation of ROMKFIEEE Transactions on Image Processing, 1997
- Spatio-temporal adaptive 3-D Kalman filter for videoIEEE Transactions on Image Processing, 1997
- Nonuniform image motion estimation using Kalman filteringIEEE Transactions on Image Processing, 1994
- Nonuniform image motion estimation in reduced coefficient transformed domainsIEEE Transactions on Image Processing, 1993
- Displacement field estimation using a coupled Gauss-Markov modelPublished by SPIE-Intl Soc Optical Eng ,1992
- Bayesian estimation of motion vector fieldsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Nonstationary AR modeling and constrained recursive estimation of the displacement fieldIEEE Transactions on Circuits and Systems for Video Technology, 1992
- Ill-posed problems in early visionProceedings of the IEEE, 1988
- Kalman filter formulation of low-Level television image motion estimationComputer Vision, Graphics, and Image Processing, 1983
- Determining optical flowArtificial Intelligence, 1981