Strain Analysis by a Total Generalized Variation Regularized Optical Flow Model
Frank Balle, Tilmann Beck, Dietmar Eifler, Jan Henrik Fitschen,, Sebastian Schuff, Gabriele Steidl

TL;DR
This paper introduces a variational optical flow model with total generalized variation regularization to accurately estimate local strain tensors from micro-structural image sequences, effectively capturing high strain regions and material cracks.
Contribution
It proposes a novel convex variational model that directly computes strain tensors, incorporating physical constraints and a coarse-to-fine strategy, outperforming existing software in local strain resolution.
Findings
The model accurately captures high strain and cracks.
The algorithm outperforms state-of-the-art software.
It effectively handles large displacements.
Abstract
In this paper we deal with the important problem of estimating the local strain tensor from a sequence of micro-structural images realized during deformation tests of engineering materials. Since the strain tensor is defined via the Jacobian of the displacement field, we propose to compute the displacement field by a variational model which takes care of properties of the Jacobian of the displacement field. In particular we are interested in areas of high strain. The data term of our variational model relies on the brightness invariance property of the image sequence. As prior we choose the second order total generalized variation of the displacement field. This prior splits the Jacobian of the displacement field into a smooth and a non-smooth part. The latter reflects the material cracks. An additional constraint is incorporated to handle physical properties of the non-smooth part for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsOptical measurement and interference techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
