Bayesian methods for optical flow estimation using a variational approximation, with applications to ultrasound

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149984" target="_blank" >RIV/00216305:26220/23:PU149984 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/10095694" target="_blank" >https://ieeexplore.ieee.org/abstract/document/10095694</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP49357.2023.10095694" target="_blank" >10.1109/ICASSP49357.2023.10095694</a>

Result language
angličtina
Original language name
Bayesian methods for optical flow estimation using a variational approximation, with applications to ultrasound
Original language description
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.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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