Diffeomorphic Particle Image Velocimetry
نویسندگان
چکیده
The existing particle image velocimetry (PIV) do not consider the curvature effect of non-straight trajectory, because it seems to be impossible obtain information from a pair images. As result, computed vector underestimates real velocity due straight-line approximation, that further causes systematic error for PIV instrument. In this work, curved trajectory between two recordings is firstly explained with streamline segment steady flow (diffeomorphic transformation) instead single vector, and idea termed as diffeomorphic PIV. Specifically, deformation field introduced describe displacement, i.e., we try find optimal field, which corresponding agrees displacement. Because variation function can approximated function, implemented iterative That says, warps images velocity, keeps rest same PIVs. Two schemes -- forward interrogation (FDDI) central (CDDI) are proposed. Tested on synthetic images, FDDI achieves significant accuracy improvement across different one-pass displacement estimators (cross-correlation, optical flow, deep learning flow). Besides, results three pairs demonstrate non-negligible CDI-based PIV, our provides larger estimation (more accurate) in fast curvy areas. combination accurate dense estimator means paves new way complex measurement.
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ژورنال
عنوان ژورنال: IEEE Transactions on Instrumentation and Measurement
سال: 2021
ISSN: ['1557-9662', '0018-9456']
DOI: https://doi.org/10.1109/tim.2021.3132999