Skip to main content
SpringerLink
Log in

3D surface analysis using coupled HMMs

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

We propose a coupled hidden Markov model (CHMM) for analysis of steel surfaces containing three-dimensional flaws. The aim is to model surface errors, which are stretched across one or more surface segments because of their strongly varying size. Due to scale on the surface, the reflection property across the intact surface changes and intensity imaging fails. Light sectioning is used to acquire the surface range data. The steel block is vibrating on the conveyor during data acquisition, which complicates robust feature extraction. After depth map recovery and feature extraction, segments of the surface are classified using CHMMs. The CHMM achieves a recognition rate of 98.57%. We compare the CHMM approach to the naïve Bayes classifier, the Hidden Markov Model, the k-nearest neighbor classifier, and to the Support Vector Machine.

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

Similar content being viewed by others

References

  1. Newman, T., Jain, A.: A survey of automated visual inspection. Comput. Vision Image Understand. 61(2), 231–262 (1995)

    Article  Google Scholar 

  2. Pernkopf, F., O'Leary, P.: Image acquisition techniques for automatic visual inspection of metallic surfaces. NDT E Int. 36(8), 609–617 (2003)

    Article  Google Scholar 

  3. Dupont, F., Odet, C., Carton, M.: Optimization of the recognition of defects in flat steel products with the cost matrices theory. NDT N Int. 30(1), 3–10 (1997)

    Article  Google Scholar 

  4. Pernkopf, F., O'Leary, P.: Visual inspection of machined metallic high-precision surfaces, special issue on applied visual inspection. EURASIP J. Appl. Signal Process. (7), 667–678 (2002)

    Article  Google Scholar 

  5. Nayar, S., Ikeuchi, K., Kanade, T.: Surface reflection: physical and geometrical perspectives. IEEE Trans. Pattern Anal. Machine Intell. 13(7), 611–634 (1991)

    Article  Google Scholar 

  6. Torrance, K., Sparrow, E.: Theory for off-specular reflection from roughened surfaces. J. Optical Soc. Am. 57(9), 1105–1114 (1967)

    Article  Google Scholar 

  7. Proceedings of SPIE. Machine Vision Applications in Industrial Inspection. URL: www.spie.org

  8. Kanade, R.: Three-Dimensional Machine Vision. Kluwer Academic Publishers, Drodrecht (1987)

    Google Scholar 

  9. Curless, B., Levoy, M.: Better optical triangulation through spacetime analysis. In: 5th International Conference on Computer Vision, pp. 987–994 (1995)

  10. IVP (Integrated Vision Products), Company. IVP Ranger SAH5 Product Information. URL: www.ivp.se.

  11. Brand, M., Oliver, N., and Pentland, A.: Coupled hidden markov models for complex action recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 994–999 (1997)

  12. Nefian, A.: Embedded Bayesian networks for face recognition. In: IEEE International Conference on Multimedia and Expo, vol. 2, pp. 133–136 (2002)

  13. Nefian, A., Liang, L., Pi, X., Liu, X., Murphy, K.: Dynamic Bayesian networks for audio-visual speech recognition. EURASIP J. Appl. Signal Process. 11, 1–15 (2002)

    Google Scholar 

  14. Long, A., Long, C.: Surface approximation and interpolation via matrix SVD. College Math. J. 32(1), 20–25 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Golub, G., Loan, C.V.: Matrix Computations, 3rd ed. The John Hopkins University Press (1996)

  16. Pernkopf, F., Pernkopf, F., O'Leary, P.: Detection of surface defects on raw milled steel blocks using range imaging. In: IT&S/SPIE 14th Symposium Electronic Imaging, pp. 170–181 (2002)

  17. Pernkopf, F.: Detection of surface defects on raw steel blocks using Bayesian network classifiers. Pattern Anal. Appl. 7(3), 333–342 (2004)

    Article  MathSciNet  Google Scholar 

  18. Gonzalez, R., Woods, R.: Digital Image Processing. Addison-Wesley, Reading (1992)

    Google Scholar 

  19. McLachlan, G., Peel, D.: Fintite Mixture Models. Wiley, New York (2000)

    Google Scholar 

  20. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood estimation from incomplete data via the EM algorithm. J. R. Stat. Soc. 30(B), 1–38 (1977)

    MathSciNet  Google Scholar 

  21. Pavlovic, V.: Dynamic Bayesian networks for information fusion with applications to human–computer interfaces. PhD thesis, University of Illinois at Urbana-Champaign (1999)

  22. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2000)

    Google Scholar 

  23. Burges, C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowledge Discov. 2(2), 121–167 (1998)

    Article  Google Scholar 

  24. Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press (2001)

Download references

Author information

Authors and Affiliations

  1. Laboratory of Signal Processing and Speech Communication, Graz University of Technology, Inffeldgasse 12, 8010, Graz, Austria

    Franz Pernkopf

Authors
  1. Franz Pernkopf
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Franz Pernkopf.

Additional information

Franz Pernkopf received his MSc (Dipl. Ing.) degree in Electrical Engineering at Graz University of Technology, Austria, in summer 1999. He earned a PhD degree from the University of Leoben, Austria, in 2002. In 2002 he was awarded the Erwin Schrödinger Fellowship. In 2004 he was a research associate at the Department of Electrical Engineering at the University of Washington, Seattle. Currently, he is an university assistant at the Signal Processing and Speech Communication Laboratory at Graz University of Technology, Austria. His research interests include graphical models, generative and discriminative learning of Bayesian network classifiers, feature selection, finite mixture models, image processing and vision, and statistical pattern recognition.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pernkopf, F. 3D surface analysis using coupled HMMs. Machine Vision and Applications 16, 298–305 (2005). https://doi.org/10.1007/s00138-005-0001-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-005-0001-3

Keywords

Search

Navigation