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- W4372260441 abstract "We develop a unified Bayesian framework for optical flow (OF) estimation that uses a variational lower bound to obtain a variational approximation of the posterior probability distribution. Our framework enables the incorporation of domain-specific knowledge as well as a quantification of the uncertainty of OF estimation, and it encompasses existing maximum a posteriori (MAP) and variational Bayes (VB) methods as special cases. We leverage this flexibility for the ultrasound modality by using ultrasound-specific likelihood functions within both MAP and VB methods. Numerical results for the problem of cardiac motion estimation demonstrate that VB methods outperform MAP methods, in addition to providing a more faithful uncertainty measure." @default.
- W4372260441 created "2023-05-07" @default.
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- W4372260441 date "2023-06-04" @default.
- W4372260441 modified "2023-09-27" @default.
- W4372260441 title "Bayesian Methods for Optical Flow Estimation Using a Variational Approximation, with Applications to Ultrasound" @default.
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- W4372260441 doi "https://doi.org/10.1109/icassp49357.2023.10095694" @default.
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