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Digital volume correlation: what are the limits to the spatialresolution?

Published online by Cambridge University Press:  22 November 2012

Hugo Leclerc ,
Jean-Noël Périé ,
François Hild  and
Stéphane Roux
Hugo Leclerc
Affiliation:
LMT-Cachan, ENS Cachan/CNRS/UPMC/UniverSud Paris, 61 avenue du Président Wilson, 94235 Cachan Cedex, France
Jean-Noël Périé
Affiliation:
Université de Toulouse; INSA, UPS, Mines Albi, ISAE, ICA (Institut Clément Ader), 133 avenue de Rangueil, 31077 Toulouse, France
François Hild*
Affiliation:
LMT-Cachan, ENS Cachan/CNRS/UPMC/UniverSud Paris, 61 avenue du Président Wilson, 94235 Cachan Cedex, France
Stéphane Roux
Affiliation:
LMT-Cachan, ENS Cachan/CNRS/UPMC/UniverSud Paris, 61 avenue du Président Wilson, 94235 Cachan Cedex, France
*
a Corresponding author:[email protected]
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Abstract

Most of the norms used in the field of digital image (and volume) correlation to registertwo images (or volumes) lead to ill-posed problems. One of the frequent solutions is toenforce a restricted kinematics requiring a compromise between the richness of thesolution (i.e., the spatial resolution) and the measurement uncertainty. An alternativeroute is to use a displacement norm that permits to alleviate this compromise by the meansof a mechanical regularization used when the gray levels do not give enough information.It is then possible to compute a displacement vector for each pixel orvoxel, inducing lower residuals (in terms of experimental data) whiledecreasing the noise sensitivity. The resolution performance of these different approachesis discussed, and compared for the analysis of a tensile test on a cast iron specimenbased on a pair of tomographic images. As representative reconstructed volumes lead to alarge number of degrees of freedom, a dedicated GPU computational strategy has beendeveloped and implemented.

Keywords

Global digital image correlationGPUregularizationCorrélation d’imagesGPUrégularisation
Type
Research Article
Information
Mechanics & Industry , Volume 13 , Issue 6: Giens 2011 , 2012 , pp. 361 - 371
Copyright
© AFM, EDP Sciences 2012

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References

B.A. Mae, The revolution in medical imaging rosen Pub. Group, New York, NY (USA), 2003
Industrial CT scanning, http://en.wikipedia.org/wiki/Industrial_CT_scanning, accessed in February, 2012
J. Baruchel, J.-Y. Buffière, E. Maire, P. Merle, G. Peix, X-Ray Tomography in Material Sciences, Hermes Science, Paris France, 2000
Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad, M., Digital volume correlation : three-dimensional strain mapping using X-ray tomography, Exp. Mech. 39 (1999) 217–226 CrossRefGoogle Scholar
Smith, T.S., Bay, B.K., Rashid, M.M., Digital volume correlation including rotational degrees of freedom during minimization, Exp. Mech. 42 (2002) 272–278 CrossRefGoogle Scholar
Bornert, M., Chaix, J.-M., Doumalin, P., Dupré, J.-C., Fournel, T., Jeulin, D., Maire, E., Moreaud, M., Moulinec, H., Mesure tridimensionnelle de champs cinématiques par imagerie volumique pour l’analyse des matériaux et des structures, Inst. Mes. Métrol. 4 (2004) 43–88 Google Scholar
Verhulp, E., van Rietbergen, B., Huiskes, R., A three-dimensional digital image correlation technique for strain measurements in microstructures, J. Biomech. 37 (2004) 1313–1320 CrossRefGoogle ScholarPubMed
Germaneau, A., Doumalin, P., Dupré, J.C., Full 3D measurement of strain field by scattered light for analysis of structures, Exp. Mech. 47 (2007) 523–532 CrossRefGoogle Scholar
Lenoir, N., Bornert, M., Desrues, J., Bésuelle, P., Viggiani, G., Volumetric digital image correlation applied to X-ray microtomography images from triaxial compression tests on argillaceous rock, Strain 43 (2007) 193–205 CrossRefGoogle Scholar
Forsberg, F., Mooser, R., Arnold, M., Hack, E., Wyss, P., 3D micro-scale deformations of wood in bending : Synchrotron radiation CT data analyzed with digital volume correlation, J. Struct. Biol. 164 (2008) 255–262 CrossRefGoogle Scholar
Roux, S., Hild, F., Viot, P., Bernard, D., Three dimensional image correlation from X-Ray computed tomography of solid foam, Comp. Part A 39 (2008) 1253–1265 CrossRefGoogle Scholar
Hild, F., Maire, E., Roux, S., Witz, J.-F., Three dimensional analysis of a compression test on stone wool, Acta Mat. 57 (2009) 3310–3320 CrossRefGoogle Scholar
Limodin, N., Réthoré, J., Buffière, J.-Y., Gravouil, A., Hild, F., Roux, S., Crack closure and stress intensity factor measurements in nodular graphite cast iron using 3D correlation of laboratory X ray microtomography images, Acta Mat. 57 (2009) 4090–4101 CrossRefGoogle Scholar
Limodin, N., Réthoré, J., Buffière, J.-Y., Hild, F., Roux, S., Ludwig, W., Rannou, J., Gravouil, A., Influence of closure on the 3D propagation of fatigue cracks in a nodular cast iron investigated by X-ray tomography and 3D Volume Correlation, Acta Mat. 58 (2010) 2957–2967 CrossRefGoogle Scholar
Rannou, J., Limodin, N., Réthoré, J., Gravouil, A., Ludwig, W., Baïetto-Dubourg, M.-C., Buffière, J.-Y., Combescure, A., Hild, F., Roux, S., Three dimensional experimental and numerical multiscale analysis of a fatigue crack, Comp. Meth. Appl. Mech. Eng. 199 (2010) 1307–1325 CrossRefGoogle Scholar
ISO/IEC guide 99-12 :2007, International Vocabulary of Metrology – Basic and General Concepts and Associated Terms, VIM, International Organization for Standardization, Geneva (Switzerland), 2007
Bergonnier, S., Hild, F., Roux, S., Digital image correlation used for mechanical tests on crimped glass wool samples, J. Strain Analysis 40 (2005) 185–197 CrossRefGoogle Scholar
Besnard, G., Hild, F., Roux, S., Finite-element displacement fields analysis from digital images : Application to Portevin-Le Châtelier bands, Exp. Mech. 46 (2006) 789–803 CrossRefGoogle Scholar
Limodin, N., Réthoré, J., Adrien, J., Buffière, J.-Y., Hild, F., Roux, S., Analysis and artifact correction for volume correlation measurements using tomographic images from a laboratory X-ray source, Exp. Mech. 51 (2011) 959–970 CrossRefGoogle Scholar
Heisenberg, W., Über den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik, Z. Phys. 43 (1927) 172–198 CrossRefGoogle Scholar
W. Heisenberg, Physikalische Prinzipien der Quantentheorie, Hirzel, Leipzig (Germany), 1930
Gabor, D., Theory of communication, J. Ins. Electl. Eng. 93 (1946) 429–457 Google Scholar
S. Mallat, A Wavelet Tour of Signal Processing, Academic Press, San Diego (California), USA, 1998
Horn, B.K.P., Schunck, B.G., Determining optical flow, Artificial Intelligence 17 (1981) 185–203 CrossRefGoogle Scholar
Mitiche, A., Bouthemy, P., Computation and analysis of image motion : A synopsis of current problems and methods, Int. J. Comp. Vision. 19 (1996) 29–55 CrossRefGoogle Scholar
M. Black, Robust Incremental Optical Flow, Ph.D. dissertation, Yale University, 1992
Odobez, J.-M., Bouthemy, P., Robust multiresolution estimation of parametric motion models, J. Visual Comm. Image Repres. 6 (1995) 348–365 CrossRefGoogle Scholar
Leclerc, H., Périé, J.-N., Roux, S., Hild, F., Voxel-scale digital volume correlation, Exp. Mech. 51 (2011) 479–490 CrossRefGoogle Scholar
T. Schiwietz, R. Westermann, GPU-PIV, edited by B. Girod, M.A. Magnor, H.-P. Seidel, in : Proceedings Vision, Modeling and Visualization Conference, Aka GmnH, 2004, pp. 151–158
Kondratieva, P., Georgii, J., Westermann, R., Petermeier, H., Kowalczyk, W., Delgado, A., A real-time model-based approach for the reconstruction fluid flows induced by microorganisms, Exp. Fluids 45 (2008) 203–222 CrossRefGoogle Scholar
F. Champagnat, A. Plyer, G.L. Besnerais, Y.L. Sant, How to calculate dense PIV vector fields at video rate, Proceedings 8th International Symposium of Particle Image Velocimetry, 2009, PIV09-0091
S. Tarashima, M. Tange, S. Someya, K. Okamoto, GPU accelerated direct cross-correlation PIV with window deformation, Proc. 15th Int Symp on Applications of Laser Techniques to Fluid Mechanics, 2010, p. 8
Asmara, R.A., Hariadi, M., Accelerating Phase Based Motion Estimation with Hierarchical Search Technique Using Parallel Threading in Graphical Processing Unit (GPU), Int. J. Comput. Sci. Network Security 9 (2009) 140–146 Google Scholar
H. Leclerc, J.-N. Périé, S. Roux, F. Hild, Integrated digital image correlation for the identification of mechanical properties, in : MIRAGE 2009, A. Gagalowicz, W. Philips, eds., (Springer, Berlin, 2009), LNCS 5496 161–171
J. Marzat, Y. Dumortier, A. Ducrot, Real-Time Dense and Accurate Parallel Optical Flow using CUDA, Proc. WSCG 2009, pp. 105–111
Mitzel, D., Pock, T., Schoenemann, T., Cremers, D., Video Super Resolution using Duality Based TV-L1 Optical Flow, in : Lecture Notes in Computer Science, 5748 (2009) 432–441 Google Scholar
Noe, K.Ø., De Senneville, B.D., Elstrøm, U.V., Tanderup, K., Sørensen, T.S., Acceleration and validation of optical flow based deformable registration for image-guided radiotherapy, Acta Oncologica 47 (2008) 1286–1293 Google Scholar
Lu, P.J., Oki, H., Frey, C.A., Chamitoff, G.E., Chiao, L., Fincke, E.M., Foale, C.M., Magnus, S.H., McArthur, W.S. Jr., Tani, D.M., Whitson, P.A., Williams, J.N., Meyer, W.V., Sicker, R.J., Au, B.J., Christiansen, M., Schofield, A.B., Weitz, D.A., Orders-of-magnitude performance increases in GPU-accelerated correlation of images from the International Space Station, J. Real-Time Image Proc. 5 (2010) 179–193 CrossRefGoogle Scholar
Ruijters, D., Ter Haar Romeny, B.M., Suetens, P., GPU-accelerated elastic 3D image registration for intra-surgical applications, Comput. Meth. Programs Biomed. 103 (2011) 104–112 CrossRefGoogle ScholarPubMed
Shackleford, J.A., Kandasamy, N., Sharp, G.C., On developing B-spline registration algorithms for multi-core processors, Phys. Med. Biol. 55 (2010) 6329–6351 CrossRefGoogle Scholar
NVIDIA CUDA C Programming Guide, NVIDIA Corporation, (http://developer.download.nvidia.com/compute/DevZone/docs/html/C /doc/CUDA_C_Programming_Guide.pdf, accessed in December 2011)
Saad, Y., van der Vorst, H.A., Iterative Solution of Linear Systems in the 20th Century, J. Comput. Appl. Math. 123 (2000) 1–33 CrossRefGoogle Scholar
Claire, D., Hild, F., Roux, S., A finite element formulation to identify damage fields : The equilibrium gap method, Int. J. Num. Meth. Eng. 61 (2004) 189–208 CrossRefGoogle Scholar
Réthoré, J., Besnard, G., Vivier, G., Hild, F., Roux, S., Experimental investigation of localized phenomena using Digital Image Correlation, Phil. Mag. 88 (2008) 3339–3355 CrossRefGoogle Scholar
F. Hild, S. Roux, Comparison of local and global approaches to digital image correlation, Exp. Mech. 2012 [in press, DOI 10.1007/s11340-012-9603-7]
Réthoré, J., Limodin, N., Buffière, J.-Y., Hild, F., Ludwig, W., Roux, S., Digital volume correlation analyses of synchrotron tomographic images, J. Strain Analysis 46 (2011) 683–695 CrossRefGoogle Scholar
Hild, F., Fanget, A., Adrien, J., Maire, E., Roux, S., Three dimensional analysis of a tensile test on a propellant with digital volume correlation, Arch. Mech. 63 (2011) 1–20 Google Scholar