Skip to main content
SpringerLink
Log in

Hybrid Stereocorrelation Using Infrared and Visible Light Cameras

  • Published:
Experimental Mechanics Aims and scope Submit manuscript

Abstract

3D kinematic fields are measured using an original stereovision system composed of one infrared (IR) and one visible light camera. Global stereocorrelation (SC) is proposed to register pictures shot by both imaging systems. The stereo rig is calibrated by using a NURBS representation of the 3D target. The projection matrices are determined by an integrated approach. The effect of gray level and distortion corrections is assessed on the projection matrices. SC is performed once the matrices are calibrated to measure 3D displacements. Amplitudes varying from 0 to 800 μm are well captured for in-plane and out-of-plane motions. It is shown that when known rigid body translations are applied to the target, the calibration can be improved when its actual metrology is approximate. Applications are shown for two different setups for which the resolution of the IR camera has been modified.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Amiable A, Chapuliot S, Contentinescu S, Fissolo A (2006) A computational lifetime prediction for a thermal shock experiment, part I: thermomechanical modeling and lifetime prediction. Fatigue Fract Eng Mater Struct 29:209–217

    Article  Google Scholar 

  2. Fissolo A, Amiable S, Ancelet O, Mermaz F, Stelmaszyk JM, Constantinescu A, Robertson C, Vincent L, Maillot V, Bouchet F (2009) Crack initiation under thermal fatigue: an overview of CEA experience. Part I: thermal fatigue appears to be more damaging than uniaxial isothermal fatigue. Int J Fatigue 31(3):587–600

    Article  Google Scholar 

  3. Fissolo A, Gourdin C, Ancelet O, Amiable S, Demassieux A, Chapuliot S, Haddar N, Mermaz F, Stelmaszyk JM, Constantinescu A, Vincent L, Maillot V (2009) Crack initiation under thermal fatigue: an overview of CEA experience: part II (of II): application of various criteria to biaxial thermal fatigue tests and a first proposal to improve the estimation of the thermal fatigue damage. Int J Fatigue 31(7):1196–1210

    Article  Google Scholar 

  4. Vincent L, Poncelet M, Roux S, Hild F, Farcage D (2013) Experimental facility for high cycle thermal fatigue tests using laser shocks. Fatigue Des 2013 Int Conf Proc 66:669–675

    Google Scholar 

  5. Esnoul C, Vincent L, Poncelet M, Hild F, Roux S (2013) On the use of thermal and kinematic fields to identify strain amplitudes in cyclic laser pulses on AISI 304L strainless steel. Presented at the Photomechanics, Montpellier (France)

  6. Charbal A, Vincent L, Hild F, Poncelet M, Dufour J-E, Roux S, Farcage D (2015) Characterization of temperature and strain fields during cyclic laser shocks. Quant Infrared Thermogr J. doi:10.1080/17686733.2015.1077544

    Google Scholar 

  7. Sutton MA, Orteu J-J, Schreier HW (2009) Image correlation for shape, motion and deformation measurements. Springer

  8. Orteu J-J (2009) 3-D computer vision in experimental mechanics. Opt Meas 47(3–4):282–291

    Google Scholar 

  9. Hild F, Roux S (2012) Digital image correlation. In: Rastogi P, Hack E (eds) Optical methods for solid mechanics : a full-field approach. Wiley-VCH, Berlin

    Google Scholar 

  10. Prakash S, Lee PY, Robles-Kelly A (2007) Stereo techniques for 3D mapping of object surface temperatures. Quant Infrared Thermogr J 4(1):63–84

    Article  Google Scholar 

  11. Rangel J, Soldan S, Kroll A (2014) 3D thermal imaging: fusion of thermography and depth cameras. Presented at the 12th international conference on quantitative infrared thermography. Bordeaux

  12. Gaussorgues G (1999) La thermographie infrarouge, 4th ed. TEC&DOC

  13. Orteu J-J, Rotrou Y, Sentenac T, Robert L (2008) An innovative method for 3-D shape, strain and temperature full-field measurement using a single type of camera: principle and preliminary results. Exp Mech 48(2):163–179

    Article  Google Scholar 

  14. Chrysochoos A, Martin G (1989) Tensile test microcalorimetry for thermomechanical behaviour law analysis. Mater Sci Eng A 108:25–32

    Article  Google Scholar 

  15. Favier D, Louche H, Schlosser P, Orgéas L, Vacher P, Debove L (2007) Homogeneous and heterogeneous deformation mechanisms in an austenitic polycrystalline Ti–50.8 at.% Ni thin tube under tension. Investigation via temperature and strain fields measurements. Acta Mater 55(16):5310–5322

    Article  Google Scholar 

  16. Schlosser P, Louche H, Favier D, Orgéas L (2007) Image processing to estimate the heat sources related to phase transformations during tensile tests of NiTi tubes. Strain 43(3):260–271

    Article  Google Scholar 

  17. Chrysochoos A, Berthel B, Latourte F, Galtier A, Pagano S, Wattrisse B (2008) Local energy analysis of high-cycle fatigue using digital image correlation and infrared thermography. J Strain Anal Eng Des 43(6):411–422

    Article  Google Scholar 

  18. Chrysochoos A (2012) Thermomechanical analysis of the cyclic behavior of materials. IUTAM Symp Full-Field Meas Identif Solid Mech 4:15–26

    Google Scholar 

  19. Bodelot L, Sabatier L, Charkaluk E, Dufrénoy P (2009) Experimental setup for fully coupled kinematic and thermal measurements at the microstructure scale of an AISI 316 L steel. Mater Sci Eng A 501(1–2):52–60

    Article  Google Scholar 

  20. Bodelot L, Charkaluk E, Sabatier L, Dufrénoy P (2011) Experimental study of heterogeneities in strain and temperature fields at the microstructural level of polycrystalline metals through fully-coupled full-field measurements by Digital Image Correlation and Infrared Thermography. Mech Mater 43(11):654–670

    Article  Google Scholar 

  21. Seghir R, Charkaluk E, Dufrénoy P, Bodelot L (2010) Thermomechanical couplings in crystalline plasticity under fatigue loading. Fatigue 2010 2(1):1155–1164

    Google Scholar 

  22. Seghir R, Bodelot L, Charkaluk E, Dufrénoy P (2012) Numerical and experimental estimation of thermomechanical fields heterogeneity at the grain scale of 316 L stainless steel. Comput Mater Sci 53(1):464–473

    Article  Google Scholar 

  23. Utz S, Soppa E, Christopher K, Schuler X, Silcher H (2014) Thermal and mechanical fatigue loading - Mechanisms of crack initiation and crack growth. Presented at the proceedings of the ASME 2014 pressure vessels & piping conference, PVP2014, Anaheim, California, USA. p. 10

  24. Pottier T, Moutrille M-P, Le Cam J-B, Balandraud X, Grédiac M (2009) Study on the use of motion compensation techniques to determine heat sources. Application to large deformations on cracked rubber specimens. Exp Mech 49(4):561–574

    Article  Google Scholar 

  25. Maynadier A, Poncelet M, Lavernhe-Taillard K, Roux S (2012) One-shot measurement of thermal and kinematic fields: InfraRed Image Correlation (IRIC). Exp Mech 52(3):241–255

    Article  Google Scholar 

  26. Maynadier A, Poncelet M, Lavernhe-Taillard K, Roux S (213) One-shot thermal and kinematic field measurements: Infra-Red Image Correlation. In T. Proulx (Ed), Application of imaging techniques to mechanics of materials and structures, volume 4. Springer New York, 2013, pp. 243–250

  27. Besnard G, Lagrange J-M, Hild F, Roux S, Voltz C (2010) Characterization of necking phenomena in high-speed experiments by using a single camera. EURASIP J Image Video Process 2010(1)

  28. Szeliski R (2010) Computer vision: algorithms and applications. Springer

  29. Dufour J-E, Beaubier B, Hild F, Roux S (2015) CAD-based displacement measurements with stereo-DIC: principle and first validations. Exp Mech 55(9):1657–1668

    Article  Google Scholar 

  30. Piegl L, Tiller W (1997) The NURBS book, 2nd ed. Springer

  31. Beaubier B, Dufour J-E, Hild F, Roux S, Lavernhe S, Lavernhe-Taillard K (2014) CAD-based calibration and shape measurement with stereoDIC. Exp Mech 54(3):329–341

    Article  Google Scholar 

  32. Brown DC (1966) Decentering distortion of lenses. Photogramm Eng 32:444–462

    Google Scholar 

  33. Brown DC (1971) Close-range camera calibration. Photogramm Eng 37:855–866

    Google Scholar 

  34. Faugueras OD, Toscani G (1989) The calibration problem for stereoscopic vision. In: Casals A (ed) Sensor devices and systems for robotics, vol 52. Springer Berlin, Heidelberg, pp 195–213

    Chapter  Google Scholar 

  35. Tsai RY (1987) Versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the shelf TV cameras and lenses. 323–344

  36. Fraser CS (1997) Digital camera self-calibration. ISPRS J Photogramm Remote Sens 52(4):149–159

    Article  Google Scholar 

  37. Fraser CS (1998) Automated processes in digital photogrammetric calibration, orientation, and triangulation. Digit Signal Process 8(4):277–283

    Article  MathSciNet  Google Scholar 

  38. Salvi J, Armangué X, Batlle J (2002) A comparative review of camera calibrating methods with accuracy evaluation. Pattern Recogn 35(7):1617–1635

    Article  MATH  Google Scholar 

  39. Dufour J-E, Hild F, Roux S (2014) Integrated digital image correlation for the evaluation and correction of optical distortions. Opt Lasers Eng 56:121–133

    Article  Google Scholar 

  40. Charbal A, Dufour J-E, Guery A, Hild F, Roux S, Vincent L, Poncelet M (2016) Integrated digital image correlation considering gray level and blur variations: application to distortion measurements of IR camera. Opt Lasers Eng 78:75–85

    Article  Google Scholar 

  41. Crammond G, Boyd SW, Dulieu-Barton JM (2013) Speckle pattern quality assessment for digital image correlation. Opt Lasers Eng 51(12):1368–1378

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LMT, ENS Cachan / CNRS / Univ. Paris Saclay, 61 Avenue du Président Wilson, F-94235, Cachan Cedex, France

    A. Charbal, J. E. Dufour, F. Hild, M. Poncelet & S. Roux

  2. CEA, DEN, DMN, SRMA, F-91191, Gif sur Yvette Cedex, France

    A. Charbal & L. Vincent

Authors
  1. A. Charbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. E. Dufour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Hild
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Poncelet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. L. Vincent
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S. Roux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Hild.

Appendix: Hybrid StereoRig at Higher Magnification

Appendix: Hybrid StereoRig at Higher Magnification

In order to have a better spatial resolution, a G1 lens may also be used for the IR camera (FLIR x6540sc, 640 × 512 pixels, pitch = 15 μm). The observed sample imposes the latter to be positioned normal to its surface (see Fig. 12) due to the low depth of field.

Fig. 12
figure 12

Hybrid stereo rig with the IR (with G1 lens) and visible light cameras where the motions are manually applied along 3 axes

Full size image

The IR camera with a G1-lens is calibrated by following the same procedure as above. Since the previous 2D target does not provide sufficient contrast at room temperature, another one is used for the distortion corrections. A grid made of copper is deposited onto a black painted surface. The difference of emissivities (i.e., low for copper and high for the black paint) provides the required contrast. According to the manufacturer the pitch is 480 μm so that a simple grid is generated. The latter is used as a reference in the boxed region of interest (ROI) of Fig. 13.

Fig. 13
figure 13

(a) Reference (numerical) grid and (b) distorted image for the IR camera

Full size image

The measured distortion fields are illustrated in Fig. 14. As observed with the visible light camera, they are vanishing in the image center and reach 1.5 pixel (or 22.5 μm) near the image borders. Even when expressed in pixels, these orders of magnitude are lower than those observed with the 50-mm lens, thereby proving that the quality of the G1-lens is higher than the 50-mm lens with the 12 mm extension ring.

Fig. 14
figure 14

Distortion fields (a) u x and (b) u y (expressed in pixel) for the IR camera with G1 lens

Full size image

Once the pictures are corrected from the estimated lens distortions, it is possible to perform the next step, which consists of determining the projection matrices. When the gray levels are assumed to be conserved, the RMS gray level residual is as high as 25.1 % of the dynamic range. When a second order field (polynomial function of order 2) is considered and one blurring kernel it is reduced to 1.2 %. The quality of the residual after GLC corrections (see Fig. 15) is an additional proof of the quality of the estimates.

Fig. 15
figure 15

Gray level residual for the determination of the projection matrices (a) without and (b) with gray level and blur corrections (see main text)

Full size image

Similar remarks and conclusions can be drawn from the present study, namely, the initial guess is closer to the matrices determined with GLC and that such corrections should be considered for a better estimation of the projection matrices.

The translational motions are performed by using a manual 3-axis stage. The prescribed and measured rigid body motions are listed in Table 5. In the present case, the integrated version (I-SC) is considered. According to the values reported in Table 5, the displacements are well captured. The difference in terms of displacement component is mostly due to misalignments between the target frame and that of the 3-axis stage.

Table 5 Prescribed and measured rigid body motions expressed in μm
Full size table

Globally, the I-SC residuals normalized by the dynamic range of the reference pictures are smaller for the visible light camera (ranging from 0.6 to 1.2 %) when compared with the IR camera (0.6 to 2.1 %) when 3D displacements are measured. These low levels and the fact that the residual map is rather uniform (Fig. 16(b)) when compared to the case with no GLCs (Fig. 16(a)) show that the registration was successful.

Fig. 16
figure 16

Gray and digital level residuals for the visible light (a) and IR (b) cameras for displacement measurements

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Charbal, A., Dufour, J.E., Hild, F. et al. Hybrid Stereocorrelation Using Infrared and Visible Light Cameras. Exp Mech 56, 845–860 (2016). https://doi.org/10.1007/s11340-016-0127-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11340-016-0127-4

Keywords

Search

Navigation