Bayesian Estimation of Motion Vector Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Application of the maximum likelihood (ML) principle and expectation-maximization (EM) technique to estimation of affine modeled image motion
New Algorithms for Variable Time Delay and Nonuniform Image Motion
New Algorithms for Variable Time Delay and Nonuniform Image Motion
Hardware implementation of optical flow constraint equation using FPGAs
Computer Vision and Image Understanding
Journal of Artificial Intelligence Research
Hardware implementation of optical flow constraint equation using FPGAs
Computer Vision and Image Understanding
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This paper focuses on the presentation and implementation of a new iterative algorithm for image motion coefficient estimation from noisy measurements based on the Expectation-Maximization (EM) technique. We also compare this algorithm with two other robust iterative algorithms. We represent the motion field by a (unitary) series expansion to obtain the motion coefficients, and show this characterization to have several virtues. First, an inherent property of motion, referred to as smoothness, is imposed. Second, the nonuniform motion estimation is reduced to the estimation of a few coefficients using the low-pass property of the motion. Finally, the motion estimation can be accomplished without the need for a motion model; in the events for which the motion model is completely unknown, the DCT representation is shown to be very effective in describing the true motion.