Abstract:We present in this paper a variational approach to accurately estimate simultaneously the velocity field and its derivatives directly from PIV image sequences. Our method differs from other techniques that have been presented in the literature in the fact that the energy minimization used to estimate the particles motion depends on a second order Taylor development of the flow. In this way, we are not only able to compute the motion vector field, but we also obtain an accurate estimation of their derivatives. … Show more
“…Numerical differential operators based on single-level differential operators have been described in various literature [29][30][31][32][33]. We shall examine the approximation of the gradient of a function based on a point of differentiation with reference to a velocity field.…”
Section: Vorticity Measurement and Statistics Of Flow Mapmentioning
“…Numerical differential operators based on single-level differential operators have been described in various literature [29][30][31][32][33]. We shall examine the approximation of the gradient of a function based on a point of differentiation with reference to a velocity field.…”
Section: Vorticity Measurement and Statistics Of Flow Mapmentioning
“…This local scheme (17) has been applied to flow field measurements by Okuno & Nakaoka (1991), Sugii et al (2000) and Yamamoto & Uemura (2009) technique has been extended for the recovery of the velocity fields and its derivative, and has been assessed on PIV images by Alvarez et al (2008). Solutions to this least squares estimation problem through an eigenvalue analysis (16) comprises the so called structure tensor approaches (Bigün et al, 1991;Jähne, 1993).…”
Variational approaches to image motion segmentation has been an active field of study in image processing and computer vision for two decades. We present a short overview over basic estimation schemes and report in more detail recent modifications and applications to fluid flow estimation. Key properties of these approaches are illustrated by numerical examples. We outline promising research directions and point out the potential of variational techniques in combination with correlation-based PIV methods, for improving the consistency of fluid flow estimation and simulation.
“…In particular, optical flow models with priors containing higher order derivatives of the flow were successfully used, e.g. in [1,6,15,20,24,32,33,35,36].…”
Section: Introductionmentioning
confidence: 99%
“…Although it seems natural to apply ideas from variational optical flow models also for strain analysis, such methods have rarely been addressed in the literature. The papers [1,15] aim at computing derivatives simultaneously to the optical flow field but are not related to engineering applications. The computation of the (Lagrangian) strain tensor by a variational method was addressed in [19].…”
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 tensile tests. We prove that the resulting convex model has a minimizer and show how a primal-dual method can be applied to find a minimizer. The corresponding algorithm has the advantage that the strain tensor is directly computed within the iteration process. Our algorithm is further equipped with a coarse-to-fine strategy to cope with larger displacements. Numerical examples with simulated and experimental data demonstrate the very good performance of our algorithm. In comparison to state-of-the-art engineering software for strain analysis our method can resolve local phenomena much better.Remark 7. The soft shrinkage (14) is obtained in the special case λ 1 = λ 2 andx 1 =x 2 .
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