Abstract

Vision-based evaluation of industrial workpieces can make efficient use of knowledge-based approaches, in particular for quality control, inspection, and accurate-measurement tasks. A possible approach is to compare real images with conceptual (synthetic) images generated by use of standard computer-aided design models, which include tolerances and take the application-specific conditions into account (e.g., the measured-calibration data). Integrated in (industrial) real-life environments, our evaluation methods have been successfully applied to on-line inspection of manufactured parts including sculptured surfaces, using structured light techniques for the reconstruction of three-dimensional shapes. Accuracies in the range 15–50 µm are routinely achieved by use of either isolated images or spatially registered image sequences.

© 2002 Optical Society of America

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    [CrossRef]
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  4. C. Costa, M. Petrou, “Automatic registration of ceramic tiles for the purpose of fault detection,” Mach. Vision Appl. 11, 225–230 (2000).
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  5. F. Arman, J. Aggarwal, “Model-Based Object Recognition Dense-Range Images.—A Review,” ACM Comput. Surv. 25, 5–43 (1993).
    [CrossRef]
  6. T. Newman, A. Jain, “A survey of automated visual inspection,” Comput. Vision Image Understand. 61, 231–262 (1995).
    [CrossRef]
  7. T. Kanade, “Region segmentation: signal versus semantics,” in Proceedings of the International Joint Conference on Pattern Recognition, Kyoto, Japan (1978), pp. 95–105.
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    [CrossRef]
  9. H.-H. Nagel, “Über die repräsentation von wissen zur auswertung von bildern,” in Angewandte Szenenanalyse, J.-P. Foith, ed., Informatik-Fachberichte No. 20 (Springer-Verlag, Berlin, 1979), pp. 3–21.
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    [CrossRef]
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  12. R. Y. Tsai, “A versatile camera calibration technique for high accuracy 3D machine vision metrology using of the shelf TV cameras and lenses,” IEEE Trans. Rob. Autom. RA-3, 323–344 (1987).
  13. R. Benjemaa, F. Schmitt, “Recalage global de plusieurs surfaces par une approche algébrique,” in 11ème Congrès Reconnaissance des Formes et Intelligence Artificielle, RFIA’98, 20-17 Janvier, LASMEA, Clermont-Ferrand, France (1998), pp. 227–396.
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    [CrossRef]
  17. G. Blais, M. Levine, “Registering multiview range data to create 3D computer objects,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 820–818 (1995).
    [CrossRef]
  18. L. Brown, “A survey of image registration techniques,” ACM Comput. Surv. 18, 325–376 (1992).
    [CrossRef]
  19. Y. Chen, G. Medioni, “Object modelling by registration of multiple range images,” Image Vision Comput. 10, 145–155 (1992).
    [CrossRef]
  20. C. Dorai, G. Wang, A. Jain, C. Mercer, “Registration and integration of multiple object views for 3D model construction,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 83–89 (1998).
    [CrossRef]
  21. A. Goshtbasy, “Three-dimensional model construction from multiview range images: survey with new results,” Pattern Recogn. 31, 1405–1414 (1998).
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    [CrossRef]
  26. M. Walker, L. Shao, R. Volz, “Estimating 3D location parameters using dual number quaternions,” Comput. Vision Image Understand. 54, 358–367 (1991).
    [CrossRef]
  27. K. Arun, T. Huang, S. Blostein, “Least squares fitting of two 3D points sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987).
    [CrossRef] [PubMed]
  28. A. Lorusso, D. W. Eggert, R. B. Fisher, “A comparison of four algorithms for estimating 3-d rigid transformation,” in Proceedings of the 6th British Machine Vision Conference (BMVC’95), D. Pycock, ed. (BMVA Press, Edinburgh, UK, 1995), pp. 237–246.
  29. A. Fleming, “Geometric relationships between toleranced features,” Artif. Intell. 37, 403–423 (1988).
    [CrossRef]
  30. J. Guilford, J. Turner, “Representational primitives for geometric tolerancing,” Comput. Aided Des. 25, 577–586 (1993).
    [CrossRef]
  31. N. P. Juster, “Modeling and representation of dimensions and tolerances: a survey,” Comput. Aided Des., 24, 25–237 (1992).
    [CrossRef]
  32. A. A. G. Requicha, “Toward a theory of geometric tolerancing,” Int. J. Rob. Res. 24, 45–60 (1983).
    [CrossRef]
  33. A. A. G. Requicha, S. C. Chan, “Representation of geometric features, tolerances, and attributes in solid modellers based on constructive geometry,” IEEE Trans. Rob. Autom. RA-24, 2356–2366 (1986).
  34. L. Rivest, C. Fortin, C. Morel, “Tolerancing a solid model with a kinematic formulation,” Comput. Aided Des. 26, 465–476 (1994).
    [CrossRef]
  35. U. Roy, C. R. Liu, “Integrated CAD frameworks: Tolerance representation scheme in a solid model,” Comput. Indus. Eng. 24, 495–509 (1993).
    [CrossRef]
  36. G. H. Tarbox, S. N. Gottschlich, “IVIS: An integrated volumetric inspection system,” Comput. Vision Image Understand. 61, 430–444 (1995).
    [CrossRef]
  37. C. Boucher, C. Daul, P. Graebling, E. Hirsch, “KBED: A knowledge-based edge detection system,” in Database and Expert System Applications, N. Revelle, A. M. Tjoa, eds., Lectures Notes in Computer Sciences No. 978 (Springer-Verlag, Berlin, 1995), pp. 344–353.
    [CrossRef]
  38. C. Boucher, “Système à base de connaissances pour la détection contrôlée des contours dans des images à niveaux de gris,” Ph.D. dissertation (Université Louis Pasteur, Strasbourg, France, July1996).
  39. G. Farin, “Curves and surfaces for CAGD: a practical guide,” 4th ed. (Academic, San Diego, Calif., 1996).

2000 (1)

C. Costa, M. Petrou, “Automatic registration of ceramic tiles for the purpose of fault detection,” Mach. Vision Appl. 11, 225–230 (2000).
[CrossRef]

1998 (2)

C. Dorai, G. Wang, A. Jain, C. Mercer, “Registration and integration of multiple object views for 3D model construction,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 83–89 (1998).
[CrossRef]

A. Goshtbasy, “Three-dimensional model construction from multiview range images: survey with new results,” Pattern Recogn. 31, 1405–1414 (1998).

1996 (1)

R. Bergevin, M. Soucy, H. Gagnon, D. Laurendeau, “Towards a general multi-view registration technique,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 540–547 (1996).
[CrossRef]

1995 (5)

T. Newman, A. Jain, “A system for 3D CAD-based inspection using range images,” Pattern Recogn. 20, 1555–1574 (1995).
[CrossRef]

T. Newman, A. Jain, “A survey of automated visual inspection,” Comput. Vision Image Understand. 61, 231–262 (1995).
[CrossRef]

G. Blais, M. Levine, “Registering multiview range data to create 3D computer objects,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 820–818 (1995).
[CrossRef]

R. Bergevin, D. Laurendeau, D. Poussart, “Registering range views of multipart objects,” Comput. Vision Image Understand. 61, 1–16 (1995).
[CrossRef]

G. H. Tarbox, S. N. Gottschlich, “IVIS: An integrated volumetric inspection system,” Comput. Vision Image Understand. 61, 430–444 (1995).
[CrossRef]

1994 (2)

L. Rivest, C. Fortin, C. Morel, “Tolerancing a solid model with a kinematic formulation,” Comput. Aided Des. 26, 465–476 (1994).
[CrossRef]

Z. Zhang, “Iterative point matching for registration of free-form curves and surfaces,” Int. J. Comput. Vision 13(2), 119–152 (1994).
[CrossRef]

1993 (3)

F. Arman, J. Aggarwal, “Model-Based Object Recognition Dense-Range Images.—A Review,” ACM Comput. Surv. 25, 5–43 (1993).
[CrossRef]

U. Roy, C. R. Liu, “Integrated CAD frameworks: Tolerance representation scheme in a solid model,” Comput. Indus. Eng. 24, 495–509 (1993).
[CrossRef]

J. Guilford, J. Turner, “Representational primitives for geometric tolerancing,” Comput. Aided Des. 25, 577–586 (1993).
[CrossRef]

1992 (4)

N. P. Juster, “Modeling and representation of dimensions and tolerances: a survey,” Comput. Aided Des., 24, 25–237 (1992).
[CrossRef]

P. Besl, N. Mc Kay, “A model for registration of 3D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

L. Brown, “A survey of image registration techniques,” ACM Comput. Surv. 18, 325–376 (1992).
[CrossRef]

Y. Chen, G. Medioni, “Object modelling by registration of multiple range images,” Image Vision Comput. 10, 145–155 (1992).
[CrossRef]

1991 (1)

M. Walker, L. Shao, R. Volz, “Estimating 3D location parameters using dual number quaternions,” Comput. Vision Image Understand. 54, 358–367 (1991).
[CrossRef]

1988 (1)

A. Fleming, “Geometric relationships between toleranced features,” Artif. Intell. 37, 403–423 (1988).
[CrossRef]

1987 (3)

K. Arun, T. Huang, S. Blostein, “Least squares fitting of two 3D points sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987).
[CrossRef] [PubMed]

R. Y. Tsai, “A versatile camera calibration technique for high accuracy 3D machine vision metrology using of the shelf TV cameras and lenses,” IEEE Trans. Rob. Autom. RA-3, 323–344 (1987).

B. Horn, “Closed-form solution of absolute orientation using unit quaternions,” J. Opt. Soc. Am. A, 4, 629–642 (1987).
[CrossRef]

1986 (1)

A. A. G. Requicha, S. C. Chan, “Representation of geometric features, tolerances, and attributes in solid modellers based on constructive geometry,” IEEE Trans. Rob. Autom. RA-24, 2356–2366 (1986).

1983 (1)

A. A. G. Requicha, “Toward a theory of geometric tolerancing,” Int. J. Rob. Res. 24, 45–60 (1983).
[CrossRef]

1980 (1)

T. Kanade, “Region segmentation: signal versus semantics,” Comput. Graph. Image Process. 13, 279–297 (1980).
[CrossRef]

Aggarwal, J.

F. Arman, J. Aggarwal, “Model-Based Object Recognition Dense-Range Images.—A Review,” ACM Comput. Surv. 25, 5–43 (1993).
[CrossRef]

Arman, F.

F. Arman, J. Aggarwal, “Model-Based Object Recognition Dense-Range Images.—A Review,” ACM Comput. Surv. 25, 5–43 (1993).
[CrossRef]

Arun, K.

K. Arun, T. Huang, S. Blostein, “Least squares fitting of two 3D points sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987).
[CrossRef] [PubMed]

Bayro-Corrochano, E. J.

E. J. Bayro-Corrochano, “Review of automated visual inspection 1983–1993, Part II: Approaches to intelligent systems,” in Intelligent Robots and Computer Vision XII: Algorithms and Techniques, D. P. Casasent, ed., Proc. SPIE2055, 159–173 (1993).
[CrossRef]

E. J. Bayro-Corrochano, “Review of automated visual inspection 1983–1993, Part I: conventional approaches,” in Intelligent Robots and Computer Vision XII: Algorithms and Techniques, D. P. Casasent, ed., Proc. SPIE2055, 128–158 (1993).
[CrossRef]

Benjemaa, R.

R. Benjemaa, F. Schmitt, “Recalage global de plusieurs surfaces par une approche algébrique,” in 11ème Congrès Reconnaissance des Formes et Intelligence Artificielle, RFIA’98, 20-17 Janvier, LASMEA, Clermont-Ferrand, France (1998), pp. 227–396.

Bergevin, R.

R. Bergevin, M. Soucy, H. Gagnon, D. Laurendeau, “Towards a general multi-view registration technique,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 540–547 (1996).
[CrossRef]

R. Bergevin, D. Laurendeau, D. Poussart, “Registering range views of multipart objects,” Comput. Vision Image Understand. 61, 1–16 (1995).
[CrossRef]

Besl, P.

P. Besl, N. Mc Kay, “A model for registration of 3D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

Blais, G.

G. Blais, M. Levine, “Registering multiview range data to create 3D computer objects,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 820–818 (1995).
[CrossRef]

Blostein, S.

K. Arun, T. Huang, S. Blostein, “Least squares fitting of two 3D points sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987).
[CrossRef] [PubMed]

Boucher, C.

C. Boucher, “Système à base de connaissances pour la détection contrôlée des contours dans des images à niveaux de gris,” Ph.D. dissertation (Université Louis Pasteur, Strasbourg, France, July1996).

C. Boucher, C. Daul, P. Graebling, E. Hirsch, “KBED: A knowledge-based edge detection system,” in Database and Expert System Applications, N. Revelle, A. M. Tjoa, eds., Lectures Notes in Computer Sciences No. 978 (Springer-Verlag, Berlin, 1995), pp. 344–353.
[CrossRef]

P. Graebling, C. Boucher, Ch. Daul, E. Hirsch, “3D sculptured surface analysis using a structured light approach,” in Videometrics IV, S. El-Hakim, ed., Proc. SPIE2598, 15–139 (1995).
[CrossRef]

Brown, L.

L. Brown, “A survey of image registration techniques,” ACM Comput. Surv. 18, 325–376 (1992).
[CrossRef]

Chan, S. C.

A. A. G. Requicha, S. C. Chan, “Representation of geometric features, tolerances, and attributes in solid modellers based on constructive geometry,” IEEE Trans. Rob. Autom. RA-24, 2356–2366 (1986).

Chen, Y.

Y. Chen, G. Medioni, “Object modelling by registration of multiple range images,” Image Vision Comput. 10, 145–155 (1992).
[CrossRef]

Costa, C.

C. Costa, M. Petrou, “Automatic registration of ceramic tiles for the purpose of fault detection,” Mach. Vision Appl. 11, 225–230 (2000).
[CrossRef]

Daul, C.

C. Boucher, C. Daul, P. Graebling, E. Hirsch, “KBED: A knowledge-based edge detection system,” in Database and Expert System Applications, N. Revelle, A. M. Tjoa, eds., Lectures Notes in Computer Sciences No. 978 (Springer-Verlag, Berlin, 1995), pp. 344–353.
[CrossRef]

Daul, Ch.

P. Graebling, C. Boucher, Ch. Daul, E. Hirsch, “3D sculptured surface analysis using a structured light approach,” in Videometrics IV, S. El-Hakim, ed., Proc. SPIE2598, 15–139 (1995).
[CrossRef]

Dorai, C.

C. Dorai, G. Wang, A. Jain, C. Mercer, “Registration and integration of multiple object views for 3D model construction,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 83–89 (1998).
[CrossRef]

Eggert, D. W.

A. Lorusso, D. W. Eggert, R. B. Fisher, “A comparison of four algorithms for estimating 3-d rigid transformation,” in Proceedings of the 6th British Machine Vision Conference (BMVC’95), D. Pycock, ed. (BMVA Press, Edinburgh, UK, 1995), pp. 237–246.

Farin, G.

G. Farin, “Curves and surfaces for CAGD: a practical guide,” 4th ed. (Academic, San Diego, Calif., 1996).

Fisher, R. B.

A. Lorusso, D. W. Eggert, R. B. Fisher, “A comparison of four algorithms for estimating 3-d rigid transformation,” in Proceedings of the 6th British Machine Vision Conference (BMVC’95), D. Pycock, ed. (BMVA Press, Edinburgh, UK, 1995), pp. 237–246.

Fleming, A.

A. Fleming, “Geometric relationships between toleranced features,” Artif. Intell. 37, 403–423 (1988).
[CrossRef]

Fortin, C.

L. Rivest, C. Fortin, C. Morel, “Tolerancing a solid model with a kinematic formulation,” Comput. Aided Des. 26, 465–476 (1994).
[CrossRef]

Gagnon, H.

R. Bergevin, M. Soucy, H. Gagnon, D. Laurendeau, “Towards a general multi-view registration technique,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 540–547 (1996).
[CrossRef]

Goshtbasy, A.

A. Goshtbasy, “Three-dimensional model construction from multiview range images: survey with new results,” Pattern Recogn. 31, 1405–1414 (1998).

Gottschlich, S. N.

G. H. Tarbox, S. N. Gottschlich, “IVIS: An integrated volumetric inspection system,” Comput. Vision Image Understand. 61, 430–444 (1995).
[CrossRef]

Graebling, P.

E. Hirsch, P. Graebling, “Vision based on-line inspection of manufactured parts: advanced concepts for quantitative quality control, standardization and integration aspects,” in Proc. IMS - Int. Conf. on Rapid Product Development, FPF, Stuttgart, Germany (1994), pp. 147–158.

P. Graebling, C. Boucher, Ch. Daul, E. Hirsch, “3D sculptured surface analysis using a structured light approach,” in Videometrics IV, S. El-Hakim, ed., Proc. SPIE2598, 15–139 (1995).
[CrossRef]

C. Boucher, C. Daul, P. Graebling, E. Hirsch, “KBED: A knowledge-based edge detection system,” in Database and Expert System Applications, N. Revelle, A. M. Tjoa, eds., Lectures Notes in Computer Sciences No. 978 (Springer-Verlag, Berlin, 1995), pp. 344–353.
[CrossRef]

Ch. Schoenenberger, P. Graebling, E. Hirsch, “Acquisition and 3D registration of image sequences for structured light based free-form surface reconstruction,” in Proceedings of the IX European Signal Processing Conference, S. Theodaris, I. Pitas, A. Stouraikis, N. Kalouptsidis, eds. Rhodes Island, Greece, (Typorama Editions, Patras, Greece, 1998), Vol. III, pp. 1281–1284.

Guilford, J.

J. Guilford, J. Turner, “Representational primitives for geometric tolerancing,” Comput. Aided Des. 25, 577–586 (1993).
[CrossRef]

Hirsch, E.

C. Boucher, C. Daul, P. Graebling, E. Hirsch, “KBED: A knowledge-based edge detection system,” in Database and Expert System Applications, N. Revelle, A. M. Tjoa, eds., Lectures Notes in Computer Sciences No. 978 (Springer-Verlag, Berlin, 1995), pp. 344–353.
[CrossRef]

Ch. Schoenenberger, P. Graebling, E. Hirsch, “Acquisition and 3D registration of image sequences for structured light based free-form surface reconstruction,” in Proceedings of the IX European Signal Processing Conference, S. Theodaris, I. Pitas, A. Stouraikis, N. Kalouptsidis, eds. Rhodes Island, Greece, (Typorama Editions, Patras, Greece, 1998), Vol. III, pp. 1281–1284.

P. Graebling, C. Boucher, Ch. Daul, E. Hirsch, “3D sculptured surface analysis using a structured light approach,” in Videometrics IV, S. El-Hakim, ed., Proc. SPIE2598, 15–139 (1995).
[CrossRef]

E. Hirsch, P. Graebling, “Vision based on-line inspection of manufactured parts: advanced concepts for quantitative quality control, standardization and integration aspects,” in Proc. IMS - Int. Conf. on Rapid Product Development, FPF, Stuttgart, Germany (1994), pp. 147–158.

Horn, B.

Huang, T.

K. Arun, T. Huang, S. Blostein, “Least squares fitting of two 3D points sets,” IEEE Trans. Pattern Anal. Mach. Intell. 9, 698–700 (1987).
[CrossRef] [PubMed]

Jain, A.

C. Dorai, G. Wang, A. Jain, C. Mercer, “Registration and integration of multiple object views for 3D model construction,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 83–89 (1998).
[CrossRef]

T. Newman, A. Jain, “A system for 3D CAD-based inspection using range images,” Pattern Recogn. 20, 1555–1574 (1995).
[CrossRef]

T. Newman, A. Jain, “A survey of automated visual inspection,” Comput. Vision Image Understand. 61, 231–262 (1995).
[CrossRef]

Juster, N. P.

N. P. Juster, “Modeling and representation of dimensions and tolerances: a survey,” Comput. Aided Des., 24, 25–237 (1992).
[CrossRef]

Kanade, T.

T. Kanade, “Region segmentation: signal versus semantics,” Comput. Graph. Image Process. 13, 279–297 (1980).
[CrossRef]

T. Kanade, “Region segmentation: signal versus semantics,” in Proceedings of the International Joint Conference on Pattern Recognition, Kyoto, Japan (1978), pp. 95–105.

Laurendeau, D.

R. Bergevin, M. Soucy, H. Gagnon, D. Laurendeau, “Towards a general multi-view registration technique,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 540–547 (1996).
[CrossRef]

R. Bergevin, D. Laurendeau, D. Poussart, “Registering range views of multipart objects,” Comput. Vision Image Understand. 61, 1–16 (1995).
[CrossRef]

Levine, M.

G. Blais, M. Levine, “Registering multiview range data to create 3D computer objects,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 820–818 (1995).
[CrossRef]

Liu, C. R.

U. Roy, C. R. Liu, “Integrated CAD frameworks: Tolerance representation scheme in a solid model,” Comput. Indus. Eng. 24, 495–509 (1993).
[CrossRef]

Lorusso, A.

A. Lorusso, D. W. Eggert, R. B. Fisher, “A comparison of four algorithms for estimating 3-d rigid transformation,” in Proceedings of the 6th British Machine Vision Conference (BMVC’95), D. Pycock, ed. (BMVA Press, Edinburgh, UK, 1995), pp. 237–246.

Mc Kay, N.

P. Besl, N. Mc Kay, “A model for registration of 3D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[CrossRef]

Medioni, G.

Y. Chen, G. Medioni, “Object modelling by registration of multiple range images,” Image Vision Comput. 10, 145–155 (1992).
[CrossRef]

Mercer, C.

C. Dorai, G. Wang, A. Jain, C. Mercer, “Registration and integration of multiple object views for 3D model construction,” IEEE Trans. Pattern Anal. Mach. Intell. 20, 83–89 (1998).
[CrossRef]

Morel, C.

L. Rivest, C. Fortin, C. Morel, “Tolerancing a solid model with a kinematic formulation,” Comput. Aided Des. 26, 465–476 (1994).
[CrossRef]

Nagel, H.-H.

H.-H. Nagel, “Über die repräsentation von wissen zur auswertung von bildern,” in Angewandte Szenenanalyse, J.-P. Foith, ed., Informatik-Fachberichte No. 20 (Springer-Verlag, Berlin, 1979), pp. 3–21.
[CrossRef]

Newman, T.

T. Newman, A. Jain, “A system for 3D CAD-based inspection using range images,” Pattern Recogn. 20, 1555–1574 (1995).
[CrossRef]

T. Newman, A. Jain, “A survey of automated visual inspection,” Comput. Vision Image Understand. 61, 231–262 (1995).
[CrossRef]

Petrou, M.

C. Costa, M. Petrou, “Automatic registration of ceramic tiles for the purpose of fault detection,” Mach. Vision Appl. 11, 225–230 (2000).
[CrossRef]

Poussart, D.

R. Bergevin, D. Laurendeau, D. Poussart, “Registering range views of multipart objects,” Comput. Vision Image Understand. 61, 1–16 (1995).
[CrossRef]

Requicha, A. A. G.

A. A. G. Requicha, S. C. Chan, “Representation of geometric features, tolerances, and attributes in solid modellers based on constructive geometry,” IEEE Trans. Rob. Autom. RA-24, 2356–2366 (1986).

A. A. G. Requicha, “Toward a theory of geometric tolerancing,” Int. J. Rob. Res. 24, 45–60 (1983).
[CrossRef]

Rivest, L.

L. Rivest, C. Fortin, C. Morel, “Tolerancing a solid model with a kinematic formulation,” Comput. Aided Des. 26, 465–476 (1994).
[CrossRef]

Roy, U.

U. Roy, C. R. Liu, “Integrated CAD frameworks: Tolerance representation scheme in a solid model,” Comput. Indus. Eng. 24, 495–509 (1993).
[CrossRef]

Schmitt, F.

R. Benjemaa, F. Schmitt, “Recalage global de plusieurs surfaces par une approche algébrique,” in 11ème Congrès Reconnaissance des Formes et Intelligence Artificielle, RFIA’98, 20-17 Janvier, LASMEA, Clermont-Ferrand, France (1998), pp. 227–396.

Schoenenberger, Ch.

Ch. Schoenenberger, P. Graebling, E. Hirsch, “Acquisition and 3D registration of image sequences for structured light based free-form surface reconstruction,” in Proceedings of the IX European Signal Processing Conference, S. Theodaris, I. Pitas, A. Stouraikis, N. Kalouptsidis, eds. Rhodes Island, Greece, (Typorama Editions, Patras, Greece, 1998), Vol. III, pp. 1281–1284.

Shao, L.

M. Walker, L. Shao, R. Volz, “Estimating 3D location parameters using dual number quaternions,” Comput. Vision Image Understand. 54, 358–367 (1991).
[CrossRef]

Soucy, M.

R. Bergevin, M. Soucy, H. Gagnon, D. Laurendeau, “Towards a general multi-view registration technique,” IEEE Trans. Pattern Anal. Mach. Intell. 15, 540–547 (1996).
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Figures (14)

Fig. 1
Fig. 1

Principle of the measuring system and of the acquisition of spatial-image sequences. The figure on top illustrates the specific correspondence problem to be solved due to the use of structured-light patterns. The bottom figure shows the principle of 3D reconstruction of measured points (the reconstructed coordinates are indicated in bold).

Fig. 2
Fig. 2

Tolerance models for ellipses (left-hand side) and line segments (right-hand side).

Fig. 3
Fig. 3

Full-3D tolerance model for a typical workpiece (left-hand side), reduced model taking the acquisition conditions into account (right-hand side).

Fig. 4
Fig. 4

Acquired image with tolerance model overlaid (above left-hand side), equivalent synthetic image (above right-hand side), and contour data retained for measurement by the expert system described by an automatically generated ascii file (below center).

Fig. 5
Fig. 5

Analyzed turbine blade and registration result of a sequence of four images.

Fig. 6
Fig. 6

Surface reconstruction (using the CATIA CAD system as a visualization tool) of a part of the turbine blade of Fig. 5 from a sequence of three images (left-hand side) and corresponding point set obtained with a CMM (right-hand side).

Fig. 7
Fig. 7

Structure of a typical turbine blade and principle of the localization of the reference frame R part.

Fig. 8
Fig. 8

Figure on the top left-hand side shows how a matrix of measurement points expressed in R senA and how a reference-coordinate system can be automatically determined with one image of the sequence. The illustration (top right-hand side) shows the points labeled as P1 (asterisks), P2 (open circles), or P3 (filled circles) used to construct the reference system. The figure below (center) pictures the result of the registration of the four measured-point sets (shaded in gray) expressed in the automatically determined common frame R part with the reference CMM point set.

Fig. 9
Fig. 9

Estimation of the quality of the CMM data set acting as ground truth (see also Fig. 8). The figure on the top left-hand side shows the raw CMM matrix of measurement points. The illustration top right-hand side shows the points labeled as planes and used to construct the reference system. (Note that the density of points kept for defining planes P1 and P3 is so high that they appear in the figure as black surface patches). The figure below indicates for each CMM point in a given plane its distance from this reference plane together with the standard deviation. The order of the points along the x-axis is arbitrary. The planes P1, P2, and P3 have the same definition as in Fig. 8.

Fig. 10
Fig. 10

Estimation of the quality of the data set obtained with the structured-light approach (compare with Fig. 9). The figure on the top left-hand side shows the raw matrix of measurement points (one image). The illustration (top right-hand side) shows the points labeled as planes and used for registration with the reference system determined by the three planes P1, P2, and P3 of Fig. 9. The figure below indicates for each measured point in a given plane its distance from the corresponding plane together with the standard deviation. The order of the points along the x-axis is arbitrary. The planes P1, P2, and P3 have the same definition as in Fig. 8.

Fig. 11
Fig. 11

Estimation of the quality of the measurements after registration with the CMM data acting as ground truth. (See also Figs. 9 and 10). The figure on the top left-hand side pictures the result of the registration of the measured-point set from Fig. 10 (shaded in gray), expressed in the automatically determined common frame R part, with the reference CMM point set. The illustration (top right-hand side) shows the points kept for evaluation of the accuracy. The figure below shows for each measured point its distance from the CMM data set, together with the standard deviation. The order of the points along the x-axis is arbitrary.

Fig. 12
Fig. 12

Estimation of the quality of the measurements under bad acquisition conditions after registration, with the CMM data acting as ground truth. The figure shows, as an example, for each measured point in plane P3 its distance from this reference plane, together with the standard deviation. The order of the points along the x-axis is arbitrary. The plane P3 has the same definition as in Fig. 8.

Fig. 13
Fig. 13

Acquired image (left-hand side) and contours kept for dimensional measurements by the inspection system.

Fig. 14
Fig. 14

Sketch of measuring robot (left-hand side) and current status of the actual device (right-hand side).

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