Parametric approach to promote a divergence-free flow in the image-based motion estimation with application to bioirrigation
DOI10.1017/S095679252200016XzbMATH Open1547.86006MaRDI QIDQ6622949
George Waldbusser, Naratip Santitissadeekorn, Erik M. Bollt, Christof Meile
Publication date: 23 October 2024
Published in: European Journal of Applied Mathematics (Search for Journal in Brave)
Bayesian inference (62F15) Monte Carlo methods (65C05) Inverse problems in geophysics (86A22) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Geostatistics (86A32) Inverse problems in optimal control (49N45)
Cites Work
- Investigation of the sampling performance of ensemble-based methods with a simple reservoir model
- Inference from iterative simulation using multiple sequences
- Variational-Bayes optical flow
- Determining optical flow
- Digital Image Processing
- A framework for estimating potential fluid flow from digital imagery
- The infinitesimal operator for the semigroup of the Frobenius-Perron operator from image sequence data: Vector fields and transport barriers from movies
- On Benchmarking Optical Flow
- Parameterizations for ensemble Kalman inversion
- Bayesian optical flow with uncertainty quantification
- Monte Carlo sampling methods using Markov chains and their applications
- Cuts in Bayesian graphical models
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