The Impact of Metrics in Mechanical Imaging

ABSTRACT Optical flow (OF) is an alternative technique to the more classical digital image correlation (DIC) methods, able to measure the motion between two images. It is a pixel‐wise method, in the sense that the displacement is defined at each pixel and its computation does not require overpixel grids. This paper examines the impact on the identified strain field, the main quantity of interest for solid mechanics, of the metrics used to quantify the conservation of the OF and to impose the regularity of the displacement. The Charbonnier and Lorentzian loss functions are inspected in this work. A new regularization approach is used to locally vary the Tikhonov parameter using a mask in order to preserve the discontinuities and encourage the appearance of local phenomena while smoothing the computed fields elsewhere. Finally, an open‐source GPU‐accelerated python code has been implemented.

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Publication Details

Journal
Strain
Published
2024-12-16
DOI
https://doi.org/10.1111/str.12494
Citations
1
Primary Topic
Advanced Vision and Imaging
Type
article
Field-Weighted Citation Impact
0.25
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article

The Impact of Metrics in Mechanical Imaging

Jean‐François Witz, Vincent Magnier, Pierre Gosselet, Ahmed Chabib
1 citations
Strain
Advanced Vision and Imaging
0.25
article

The Impact of Metrics in Mechanical Imaging

Jean‐François Witz, Vincent Magnier, Pierre Gosselet, Ahmed Chabib
article en
1 citations

Abstract

ABSTRACT Optical flow (OF) is an alternative technique to the more classical digital image correlation (DIC) methods, able to measure the motion between two images. It is a pixel‐wise method, in the sense that the displacement is defined at each pixel and its computation does not require overpixel grids. This paper examines the impact on the identified strain field, the main quantity of interest for solid mechanics, of the metrics used to quantify the conservation of the OF and to impose the regularity of the displacement. The Charbonnier and Lorentzian loss functions are inspected in this work. A new regularization approach is used to locally vary the Tikhonov parameter using a mask in order to preserve the discontinuities and encourage the appearance of local phenomena while smoothing the computed fields elsewhere. Finally, an open‐source GPU‐accelerated python code has been implemented.

StrainVol. 61(1)
Centre National de la Recherche Scientifique (FR), Université de Lille (FR), Laboratoire de Mécanique, Multiphysique, Multiéchelle (FR), École Centrale de Lille (FR)
Openalex Percentile: Top 55%
Advanced Vision and Imaging
0.25
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