Skip to main content
SpringerLink
Log in

3-D Sensing for Industrial Computer Vision

  • Chapter
Advances in Machine Vision

Part of the book series: Springer Series in Perception Engineering ((SSPERCEPTION))

Abstract

Computer vision is becoming an important issue in many industrial applications such as automatic inspection of manufactured parts, robotic manipulations, autonomous vehicle guidance, and automatic assembly. Since these applications are performed in a three-dimensional world, it is imperative to gather reliable information on the 3-D structure of the scene. Range-finder cameras are usually used to collect 3-D data. This chapter presents a review of various range-finding techniques. Early designs and more recent developments are discussed along with a critical assessment of their performances. Purely optical techniques are not covered.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Agin, G.J., and Binford, T.O. 1973. Computer description of curved objects. In Proc. Intl. Joint Conference on Artificial Intelligence (Stanford, Calif., Aug. 20–23), IJCAI, 629–640.

    Google Scholar 

  • Bair, M.E., Sampson, R., and Zuk, R. 1986. Three-dimensional imaging and applications. In Proc. SPIE 1986 Cambridge Symposium on Optics and Optoelectronics Advances in Intelligent Systems, vol. 726 (Cambridge, Mass., Oct. 28–31), 264–274.

    Google Scholar 

  • Bajcsy, R., and Lieberman, L. 1976. Texture gradient as a depth cue. Computer, Graphics and Image Processing 5, 52–67.

    Article  Google Scholar 

  • Ballard, D.H., and Brown, C. 1982. Computer vision. Prentice-Hall, Englewood Cliffs, N.J., 481–483.

    Google Scholar 

  • Blais, F., and Rioux, M. 1986a. “Real-time numerical peak detector,” Signal Processing 11, 145–155.

    Article  Google Scholar 

  • Blais, F., and Rioux, M. 1986b. BIRIS: A simple 3D sensor. In Proc. SPIE 1986 Cambridge Symposium on Optics and Optoelectronics Advances in Intelligent Systems, vol. 728 (Cambridge, Mass., Oct. 30–31), 235–242.

    Google Scholar 

  • Blais, F., Rioux, M., and Poussart, D. 1985. A very compact 3-D camera for robotic application. In Proc. 1985 Optical Society of America Topical Meeting on Machine Vision.

    Google Scholar 

  • Boehnlein, A.J., and Harding, K.G. 1986. Adaptation of a parallel architecture computer to phase shifted Moire interferometry. In Proc. SPIE 1986 Symposium on Optics and Optoelectronics Advances in Intelligent Systems, vol. 728, (Cambridge, Mass., Oct. 30–31), 183–194.

    Google Scholar 

  • Boyer, K.L., Vayda, A.J., and Kak, A.C. 1986. Robotic manipulation experiments using structural stereopsis for 3D vision. IEEE Expert 1, 3 (Fall), 73–94.

    Article  Google Scholar 

  • Brown, M.K. 1985. Feature extraction techniques for recognizing solid objects with ultrasonic range sensor. IEEE J. Robotics and Automation RA-I, 4 (Dec.), 191–205.

    Article  Google Scholar 

  • Courtney, J. W., and Aggarwal, J.K. 1983 Robot guidance using computer vision. In Proc. IEEE Conference on Trends and Applications (Gaithersburg, Md., May 25–26), IEEE, New York, 57–62.

    Google Scholar 

  • De Vriendt, A.B., 1984 Heat Transfer. An introduction to thermal radiation 2 (in French), Gaëtan Morin Ed., Canada, 15–16.

    Google Scholar 

  • Duda, R.O., and Hart, P.E. 1973 Pattern classification and scene analysis. Wiley, New York, 401–402.

    MATH  Google Scholar 

  • Duda, R.O., Nitzan, D., and Barrett, P. 1979. Use of range and reflectance data to find planar surface regions. IEEE Trans. on Pattern Analysis and Machine Intelligence. PAMI-1, 3 (Jul.) 259–271.

    Article  Google Scholar 

  • Duncan, J.P., Dean, D.P., and Pate, G.C. 1980. Moiré contourography and computer aided replication of human anatomy. Engrg. in Medicine 9, 1, 29–36.

    Article  Google Scholar 

  • Duret, F., and Blouin, J.L. 1986. De l’empreinte optique à la conception et la fabrication assistees par ordinateur d’une couronne dentaire. J Dentaire du Québec XXIII (Apr.), 177–180.

    Google Scholar 

  • Fischler, M.A., and Bolles, R.C. 1981. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6 (June), 381–395.

    Article  MathSciNet  Google Scholar 

  • Gibson, J.J. 1966. The senses considered as perceptual systems. Houghton-Mifflin, Boston, Mass.

    Google Scholar 

  • Grimson, W.E.L. 1981. From images to surfaces-A computational study of the human visual system. MIT Press, Cambridge, Mass, 15–61.

    Google Scholar 

  • Hall, E.L., Tio, J.B.K., McPherson, C.A., and Sadjadi, F.A. 1982. Measuring curved surfaces for robot vision. Computer 15, 12, 42–54.

    Article  Google Scholar 

  • Horn, B.K.P. 1970. Shape from shading: A method for obtaining the shape of a smooth opaque object from one view. MIT Project MAC Technical Report, MAC TR-79, M.I.T., Cambridge, Mass.

    Google Scholar 

  • Horn, B.K.P. 1975. Obtaining shape from shading information. In The psychology of computer vision (P.H. Winston, Ed.), McGraw-Hill, New York, 115–155.

    Google Scholar 

  • Horn, B.K.P. 1985. The variational approach to shape from shading, MIT AI Laboratory Memo 813, Mar. M.I.T., Cambridge, Mass.

    Google Scholar 

  • Horn, B.K.P. 1986. Robot vision. MIT Press, McGraw-Hill, Cambridge, Mass., 248–250.

    Google Scholar 

  • Idesawa, M., Yatagi, T., and Soma, T. 1977. Scanning Moiré method and automatic measurement of 3-D shapes. Appl. Opt. 16, 8 (Aug.), 2152, 2162.

    Article  Google Scholar 

  • Ikeuchi, K., and Horn, B.K.P. 1981. Numerical shape from shading and occluding boundaries. Artificial Intell. 17, 141–184.

    Article  Google Scholar 

  • Inokuchi, S., Sato, K., and Matsuda, F. 1984. Range imaging system for 3D object recognition. In Proc. 7th Intl. Conference on Pattern Recognition (Montreal, Canada, Jul. 30–Aug. 2) IAPR, IEEE, New York, 806–808.

    Google Scholar 

  • Ishii, M., and Nagata, T. 1976. Feature extraction of three-dimensional objects and visual processing in a hand-eye system using a laser tracker. In Pattern Recognition, Vol. 8. Pergamon Press, New York, 227–237.

    Google Scholar 

  • Jalkio, J.A., Kim, R.C., and Case, S.K. 1985. Three-dimensional inspection using multistripe structured light. Opt. Engrg. 24, 6, 966–974.

    Google Scholar 

  • Jarvis, R.A. 1980. A mobile robot for computer vision research. In Proc. 3rd Australian Computer Science Conference, (Canberra, Australia, Jan. 31–Feb. 1), A.N.V. Canberra, A.C.T., 39–51.

    Google Scholar 

  • Jarvis, R.A. 1983. A perspective on range finding techniques for computer vision. IEEE Trans. on Pattern Analysis and Machine Intell. PAMI-5, 2 (Mar.), 122–139.

    Article  Google Scholar 

  • Julesz, B. 1964. Binocular depth perception without familiarity clues. Sci. 145, 356–362.

    Article  Google Scholar 

  • Lessard, J., Laurendeau, D., and Girard, P., 1986. Computer-vision based estimation of the position of a robot in its working environment. In Proc. IEEE Conference on Power Industry Computer Application PICA’ 87 (Montreal, Canada, May 18–22), IEEE Power Engineering Society, New York, 94–100.

    Google Scholar 

  • Levine, M.D., O’Handley, D.A., and Yagi, G.M., 1973. Computer determination of depth maps. Computer, Graphics and Image Processing 2, 134–150.

    Article  Google Scholar 

  • Luh, J.Y.S., and Klaasen, J.A. 1985. A three-dimensional vision by off-shelf system with multi-cameras. IEEE Trans. on Pattern Analysis and Machine Intell. PAMI-7, 1 (Jan.) 35–45.

    Article  Google Scholar 

  • Magee, M.J., and Aggarwal, J.K. 1984. Determining the position of a robot using a single calibration object. In Proc. IEEE Conference on Robotics (Atlanta, Ga., Mar. 13–15), IEEE Computer Society, Silver Springs, Md., 140–149.

    Google Scholar 

  • Magee, M.J., Boyter, B.A., Chien, C.H., and Aggarwal, J.K. 1985. Experiments in intensity guided range sensing recognition of three-dimensional objects. IEEE Trans. on Pattern Analysis and Machine Intell. PAMI-7, 6 (Nov.), 629–637.

    Article  Google Scholar 

  • Maldague, X., Poussart, D., Laurendeau, D., and April, R. 1986. Tridimensional form acquisition apparatus. In Proc. SPIE Inti. Symposium on Optical and Optoelectronic Applied Sciences and Engineering, vol. 665 (Quebec, Canada, June), 200–208.

    Google Scholar 

  • Mandelbrot, B.B. 1982. The fractal geometry of nature. Freeman, San Francisco, Calif.

    MATH  Google Scholar 

  • Marr, D. 1981. VISION: A computational investigation in the human representation and processing of visual information. W.H. Freeman, San Francisco, Calif., 240–250.

    Google Scholar 

  • Marr, D., and Hildreth, H. 1980. Theory of edge detection. Proc. Roy. Soc. London B207, 187–217.

    Google Scholar 

  • Marr, D., and Poggio, T. 1979. A theory of human stereo vision. Proc. Roy. Soc. London B204, 301–328.

    Google Scholar 

  • Martins, H.A., Birk, J.R., and Kelley, R.B., 1981. Camera models based on data from two calibration planes. Computer, Graphics and Image Processing 17, 173–180.

    Article  Google Scholar 

  • McVey, E.S., Drake, K.C., and Inigo, R.M. 1986. Range measurements by a mobile robot using a navigation line. IEEE Trans. on Pattern Analysis and Machine Intel. PAMI-8, 1 (Jan.), 105–109.

    Article  Google Scholar 

  • Miller, R.K. 1984. 3D machine vision. SEAl Technical Publication and Technical Insight, SEAL, Madison, Ga.

    Google Scholar 

  • Nitzan, D., Brain, A.E., and Duda, R.O. 1977. The measurement and use of registered reflectance and range data in scene analysis. Proc. IEEE 65, 2, 206–220.

    Article  Google Scholar 

  • Pentland, A.P. 1986. Shading into texture. Artificial Intell. 29, 147–170.

    Article  MathSciNet  MATH  Google Scholar 

  • Posdamer, J.L., and Altshuler, M.D. 1982. Surface measurement by space-encoded projected beam systems. Computer, Graphics and Image Processing 18, 1.

    Google Scholar 

  • Reinsch, C.H. 1967. Smoothing by spline functions. Numerische Mathematik 10, 177–183.

    Article  MathSciNet  MATH  Google Scholar 

  • Rioux, M. 1984. Laser range finder based on synchronized scanners. Appl. Op. 23, 21 (Nov.), 3837–3844.

    Article  Google Scholar 

  • Rioux, M., and Blais, F. 1986. Compact three-dimensional camera for robotic applications. J. Op. Soc. Amer. 3 (Sept.), 1518–1521.

    Article  Google Scholar 

  • Shen, H.C., and Wong, K.C., 1983. Generalized texture representation and metric. Computer Vision, Graphics and Image Processing 23, 187–206.

    Article  Google Scholar 

  • Shirai, Y. 1972. Recognition of polyhedrons using a range finder. Pattern Recognition 4, 243–250.

    Article  Google Scholar 

  • Strat, T.M., and Fischler, M.A. 1986. One-eyed stereo: A general approach to modeling 3D scene geometry. IEEE Trans. on Pattern Analysis and Machine Intell. PAMI-8, 6 (Nov.), 730–741.

    Article  Google Scholar 

  • Taylor, J.P., and Inigo, R.M. 1984. Camera calibration for binary vision systems. Proc. 3rd Canadian Conference on CAD/CAM and Robotics. (Toronto, Canada, June 19–21), CASA-SME, 4.1–4.6.

    Google Scholar 

  • Tsai, R.Y. 1986. An efficient and accurate camera calibration technique for 3D machine vision. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, (Miami, Fla. June), IEEE Computer Society, Washington, D.C., 364–374.

    Google Scholar 

  • Vuylsteke, P., and Oosterlinck, A., 1986. 3-D perception with a single binary coded illumination pattern. In Proc. SPIE 1986 Cambridge Symposium on Optics and Optoelectronics Advances in Intelligent Systems, vol. 728 (Cambridge, Mass., Oct. 30–31), 195–202.

    Google Scholar 

  • Witkin, A.P. 1981. Recovering surface shape and orientation from texture. Artificial Intell. 17, 17–45.

    Article  Google Scholar 

  • Woodham, R.J. 1979. Relating properties of surface curvature to image intensity. In Proc. Intl. Joint Conference on Artificial Intelligence. (Tokyo, Japan, Aug. 20–23), HCAI, 971–977.

    Google Scholar 

  • Woodham, R.J. 1981. Analysing images of curved surfaces. Artificial Intell. 17, 117–140.

    Article  Google Scholar 

  • Woodham, R.J. 1984. Photometric method for determining shape from shading. Image understanding (S. Ullman and W. Richards, Eds.), Ablex, N.J., Chapter 4, 97–125.

    Google Scholar 

  • Yachida, M., Tsuji, S., and Huang, X.-Q. 1982. WIRESIGHT-A computer vision system for 3D measurement and recognition of flexible wire using cross-stripe light. In Proc. 6th IEEE Inti. Conference on Pattern Recognition (Munich, W. Germany, Oct. 19–22), IEEE Computer Society, Silver Spring, Md., 220–222.

    Google Scholar 

  • Yakimovsky, Y., and Cunningham, R. 1978. A system for extracting three-dimensional measurements from a stereo pair of TV cameras. Computer, Graphics and Image Processing 7, 195–210.

    Article  Google Scholar 

Download references

Authors
  1. Denis Poussart
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Denis Laurendeau
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IBM Almaden Research Center, 650 Harry Road, San Jose, CA, 95120-6099, USA

    Jorge L. C. Sanz

Rights and permissions

Reprints and permissions

Copyright information

© 1989 Springer-Verlag New York Inc.

About this chapter

Cite this chapter

Poussart, D., Laurendeau, D. (1989). 3-D Sensing for Industrial Computer Vision. In: Sanz, J.L.C. (eds) Advances in Machine Vision. Springer Series in Perception Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4532-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-4532-2_3

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-8866-4

  • Online ISBN: 978-1-4612-4532-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation