Experimental Mechanics

, Volume 44, Issue 3, pp 278–288 | Cite as

Advances in light microscope stereo vision

  • H. W. Schreier
  • D. Garcia
  • M. A. Sutton


The increasing research focus on small-scale mechanical systems has generated a need for deformation and strain measurement systems for microscale applications. Optical measurement systems, such as digital image correlation, present an obvious choice due to their non-contacting nature. However, the transfer of measurement technology developed for macroscale applications to the microscale presents unique challenges due to the differences in the required highmagnification optics. In this paper we illustrate the problems involved in calibrating a stereo microscope using traditional techniques and present a novel methodology for acquiring accurate, three-dimensional surface shape and deformation data on small-scale specimens.

Experimental results demonstrate that stereo microscope systems can be accurately and reliably calibrated using a priori distortion estimation techniques in combination with traditional bundle-adjustment. The unique feature of the present methodology is that it does not require a precision calibration target but relies solely on point correspondences obtained by image correlation. A variety of experiments illustrate the measurement performance of a stereo microscope system. It is shown that the surface strains obtained from the full-field, three-dimensional measurements on tensile specimens undergoing large rigid-body motions are within ±50 microstrain of strain gage measurements for strains ranging from 0 to 2000 microstrain.

Key Words

Stereo microscope stereo vision accurate stereo calibration procedure digital image correlation three-dimensional surface displacement measurement 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sutton, M.A., Wolters, W.J., Peters, W.H., Ranson, W.F., andMcNeill, S.R., “Determination of Displacements Using an Improved Digital Correlation Method,”Image Vision Comput.,21,133–139 (1983).CrossRefGoogle Scholar
  2. 2.
    Khan-Jetter, Z.L. andChu, T.C., “Three-dimensional Displacement Measurements Using Digital Image Correlation and Photogrammic Analysis” EXPERIMENTAL MECHANICS,30 (1),10–16 (1990).CrossRefGoogle Scholar
  3. 3.
    Luo, P.F., Chao, Y.J., Sutton, M.A., andPeters, W.H., “Accurate Measurement of Three-dimensional Deformable and Rigid Bodies Using Computer Vision,” EXPERIMENTAL MECHANICS,33 (2),123–132 (1993).CrossRefGoogle Scholar
  4. 4.
    Luo, P.F., Chao, Y.I., andSutton, M.A., “Application of Stereo Vision to 3D Deformation analysis in Fracture Mechanics,”Opt. Eng.,33,981 (1994).CrossRefGoogle Scholar
  5. 5.
    Helm, J.D., McNeill, S.R., andSutton, M.A., “Improved 3D Image Correlation for Surface Displacement Measurement,”Opt. Eng.,35 (7),1911–1920 (1996).CrossRefGoogle Scholar
  6. 6.
    Orteu, J.-J., Garric, V., and Devy, M., “Camera Calibration for 3D Reconstruction: Application to the Measure of 3D Deformations on Sheet Metal Parts,” in European Symposium on Lasers, Optics and Vision in Manufacturing, Munich, Germany, June (1997).Google Scholar
  7. 7.
    Synnergren, P. andSjödahl, M., “A Stereoscopic Digital Speckle Photography System for 3D Displacement Field Measurements,”Opt. Lasers Eng.,31,425–443 (1999).CrossRefGoogle Scholar
  8. 8.
    Galanulis, K. and Hofimann, A., “Determination of Forming Limit Diagrams Using An Optical Measurement System,” in International Conference on Sheet Metal, Erlangen, Germany, September, 245–252 (1999).Google Scholar
  9. 9.
    Sutton, M.A. McNeill, S.R., Helm, J.D., and Schreier, H.W., “Computer Vision Applied to Shape and Deformation Measurement”, in International Conference on Trends in Optical Non-Destructive Testing and Inspection, Lugano, Switzerland, 571–589 (2000).Google Scholar
  10. 10.
    Hemmled, M. and Schubert, M., “Digital Microphotogrammetry—Determination of the Topography of Microstructures by Scanning Electron Microscope,” in Second Turkish-German Joint Geodetic Days, Berlin, Germany, May, 745–752 (1997).Google Scholar
  11. 11.
    Lacey, A.J., Thacker, S., Crossley, S., andYates, R.B., “A Multi-Stage Approach to the Dense Estimation of Disparity from Stereo SEM Images,”Image Vision Comput.,16,373–383 (1998).CrossRefGoogle Scholar
  12. 12.
    Pouchou, J.-L., “Prises de vues stéréographiques au MEB: difficultés pratiques, sources d'erreur,” in Colloque de la Société Française des Microscopies, Saclay, France, June (1999).Google Scholar
  13. 13.
    Richards, R.G., Wieland, M., andTextor, M., “Advantages of Stereo Imaging of Imaging of Metallic Surfaces with Lois Voltage Backscattered Electrons in a Field Emission Scanning Electron Microscope,”J. Microscopy,199,115–123 (2000).CrossRefGoogle Scholar
  14. 14.
    Vignon, F., Le Besnerais, G., Boivin, D., Pouchou, J.L., and Quan, L., “3D Reconstruction from Scanning Electron Miscroscopy Using Stereovision and Self-calibration,” in Physics in Signal and Image Processing, Marseille, France, January (2001).Google Scholar
  15. 15.
    Sutton, M.A., Chae, T.L., Turner, J.L., and Bruck, H.A., “Development of a Computer Vision Methodology for the Analysis of Surface Deformations in Magnified Images,” in MiCon 90: Advances in Video Technology for Microstructural Control, ASTM STP 1094. Philadelphia, PA, 109–132 (1990).Google Scholar
  16. 16.
    Mazza, E., Danuser, G., andDual, J., “Light Optical Measurements in Microbars with Nanometer Resolution,”Microsyst. Technol.,2,83–91 (1996).CrossRefGoogle Scholar
  17. 17.
    Mitchell, H.L., Kniest, H.T., andWon-Jin, O., “Digital Photogrammetry and Microscope Photographs,”Photogrammetric Record,16 (94),695–704 (1999).CrossRefGoogle Scholar
  18. 18.
    Faugeras, O., Three-dimensional Computer Vision: A Geometric Viewpoint, MIT Press, Cambridge, MA (1993).Google Scholar
  19. 19.
    Beyer, H.A., “Accurate Calibration of CCD Cameras,” in Conference on Computer Vision and Pattern Recognition (1992).Google Scholar
  20. 20.
    Weng, J., Cohen, P., andHerniou, M., “Camera Calibration with Distortion Models and Accuracy Evaluation,”IEEE Trans. Pattern Anal. Mach. Intell.,14 (10),965–980 (1992).CrossRefGoogle Scholar
  21. 21.
    Faugeras, O. and Toscani, G., “Camera Calibration for 3D Computer Vision,” in International Workshop on Machine Vision and Machine Intelligence, Tokyo, Japan, February, 240–247 (1987).Google Scholar
  22. 22.
    Devy, M., Garric, V., and Orteu, J.J., “Camera Calibration from Multiple Views of a 2D Object Using a Global Nonlinear Minimization Method,” in International Conference on Intelligent Robots and Systems, Grenoble, France, September (1997).Google Scholar
  23. 23.
    Zhang, Z., “A Flexible New Technique for Camera Calibration,” Technical Report MSR-TR-98-71, Microsoft Research, December (1998). Updated March 1999.Google Scholar
  24. 24.
    Garcia, D., Orteu, J.-J., and Devy, M., “Accurate Calibration of a Stereovision Sensor: Comparison of Different Approaches,” in Workshop on Vision, Modeling, and Visualization, Saarbrücken, Germany, November, 25–32 (2000).Google Scholar
  25. 25.
    Triggs, B., McLauchlan, P., Hartley, R., and Fitzgibbon, A., “Bundle Adjustment—A Modern Synthesis,” in Vision Algorithms, Corfu, Greece (1999).Google Scholar
  26. 26.
    Li, M., andLavest, J.-M., “Some Aspects of Zoom Lens Camera Calibration,”IEEE Trans. Pattern Anal. Mach. Intell.,18 (10),1105–1110 (1996).Google Scholar
  27. 27.
    Ravn, O., Andersen, N.A., and Sorensen, A.T., “Auto-calibration in Automation Systems using Vision,“ in International Symposium on Experimental Robotics, Japan, 206–218 (1993).Google Scholar
  28. 28.
    Peuchot, B., “Camera Virtual Equivalent Model −0.01 Pixel Detector,” in International Conference IEEE EMBS, Satellite Symposium on 3D Advanced Image Processing in Medicine, Rennes, France, November, 41–45 (1992).Google Scholar
  29. 29.
    Brand, P., “Reconstruction Tridimensionnelle à partir d'une caméra en mouvement: de l'influence de la précision”,Ph.D. Thesis, Claude Bernard University, Lyon I, France, October (1995).Google Scholar
  30. 30.
    Schreier, H.W., “Calibrated Sensor and Method for Calibrating Same,” Patent Pending, November (2002).Google Scholar
  31. 31.
    Ayache, N. and Hansen, C., “Rectification of Images for Binocular and Trinocular Stereovision,” in International Conference on Pattern Recognition, Rome, Italy, 11–16 (1988).Google Scholar
  32. 32.
    Correlates Solutions Inc. and Garcia, D., Vic2D and Vic3D softwares, (2002).Google Scholar
  33. 33.
    Brown, D.C., “The Bundle Adjustment—Progress and Prospects,”Int Archives Photogrammetry,21 (3) (1976).Google Scholar
  34. 34.
    Kraus, K., Photogrammetry, Vol. 1: Fundamentals and Standard Processes, Dümmler, Bonn (1997).Google Scholar
  35. 35.
    Kraus, K., Photogrammetry, Vol. 2: Advanced Methods and Applications, Dümmler, Bonn (1997).Google Scholar
  36. 36.
    Lavest, J.-M., Viala, M., and Dhome, M., “Do we Really Need An Accurate Calibration Pattern to Achieve a Reliable Camera Calibration?” in European Conference on Computer Vision, Freiburg, Germany, 158–174 (1998).Google Scholar
  37. 37.
    Hartley, R.L. andSturm, P., “Triangulation,”Comput. Vision Image Understanding,68 (2),146–157 (1997).CrossRefGoogle Scholar
  38. 38.
    Helm, J.D., McNeill, S.R., andSutton, M.A., “Deformations in Wide, Center-notched, Thin Panels, Part I: Three-dimensional Shape and Deformation Measurements by Computer Vision”,Opt. Eng.,42 (5),1293–1305 (2003).CrossRefGoogle Scholar
  39. 39.
    Helm, J.D., McNeill, S.R., andSutton M.A., “Deformations in Wide, Center-notched, Thin Panels, Part II: Finite Element Analysis and Comparison to Measurements,”Opt. Eng.,42 (5),1306–1320 (2003).CrossRefGoogle Scholar
  40. 40.
    Schreier, H.W. andSutton, M.A., “Effect of Higher-order Displacement Fields on Digital Image Correlation Displacement Component Estimates,” EXPERIMENTAL MECHANICS,42 (3),303–311 (2002).Google Scholar
  41. 41.
    Sowerby, R., Duncan, J.L., andChu, E., “The Modelling of Sheet Metal Stamping,”Int. J. Mech. Sci.,28 (7),415–430 (1986).CrossRefGoogle Scholar
  42. 42.
    Marciniak, Z. andDuncan, J.L., The Mechanics of Sheet Metal Forming, Edward Arnold.London (1992).Google Scholar
  43. 43.
    Schreier, H., Braasch, J., andSutton, M.A., “Systematic Errors in Digital Image Correlation Caused by Intensity Interpolation,”Opt. Eng.,39 (11),2915–2921 (2000).CrossRefGoogle Scholar
  44. 44.
    Garcia, D., “Mesure de formes et de champs de déplacements tridimensionnels par stéréo-corrélation d'images,” Ph.D. Thesis,Institut National Polytechnique de Toulouse, France, December (2001).Google Scholar

Copyright information

© Society for Experimental Mechanics 2004

Authors and Affiliations

  • H. W. Schreier
  • D. Garcia
    • 1
  • M. A. Sutton
    • 2
  1. 1.Ecole Mines des Albi in FranceFrance
  2. 2.Department of Mechanical EngineeringUniversity of South CarolinaColumbiaUSA

Personalised recommendations