Skip to main content
SpringerLink
Log in

A cellular automata model for edge relaxation

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

An edge detection method based on a fuzzy cellular automata model which serves as the relaxation labeling process constraint is described. An initial estimate of edge locations is made and the remaining ambiguities are resolved by thinning and enhancing the edges through several iterations. An efficient fixed step algorithm is presented and its performance is evaluated for different noise level images. The method is useful for the detection of linear image features in three-dimensional robot vision systems.

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. Ballard, D.H. and Brown, C.M.,Computer Vision, Prentice-Hall, 1982.

  2. Burks, A.W. (ed),Essays on Cellular Automata, Univ. Illinois Press,1970.

  3. Burns. B.J., Hanson. A.R. and Riseman. E.M., Extracting straight linesIEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (4), 425–455 (1986).

    Google Scholar 

  4. Canny. J., A computational approach to edge detection,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986).

    Google Scholar 

  5. Duff, M.J.B. and Watson, D.M., CLIP3: A cellular logic image processor, in.New Concepts and Technologies in Parallel Information Processing (ed. E.R. Caianiello), Noordhoff, 1975.

  6. Fekete. G., Eklundh. J. and Rosenfeld. A., Relaxation: Evaluation and applications,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-3 (4), 459–469 (1981).

    Google Scholar 

  7. Fountain, T.J., CLIP4: A progress report, inLanguages and Architectures for Image Processing, (eds. M.J.B. Duff and S. Levialdi), Academic Press, 1981.

  8. Glazer, C., Multilevel relaxation in low-level computer vision, inMultiresolution Image Processing and Analysis, (ed. A. Rosenfeld), Springer, 1984.

  9. Haralick. R.M., Digital step edges from zero crossing of second directional derivatives,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6(1), 58–78 (1984).

    Google Scholar 

  10. Horn, B.K.P.,Robot vision, MIT Press, 1986.

  11. Hummel. R.A. and Zucker. S. W., On the foundations of relaxation labeling processes,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5(3), 267–287 (1983).

    Google Scholar 

  12. Koch. M.W. and Kashyap. R.L., Using polygons to recognize and locate partially occluded objects,IEEE Trans. Pattern. Anal. Mach. Intell. PAMI-9(4) 483–494 (1983).

    Google Scholar 

  13. Kundu. A. and Mitra. S.K., A new algorithm for image edge extraction using a statistical classifier approach,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-9(4), 569–577 (1987).

    Google Scholar 

  14. Levine, M.D.,Vision in Man and Machine, McGraw-Hill, 1985.

  15. Lowe, D.G.,Perceptual Organization and Visual Recognition, Kluwer, 1985.

  16. Lowe, D.G., Three-dimesional object recognition from single two-dimensional images, TR 202 (RR 62), Courant Institute of Mathematical Sciences, NYU, 1986.

  17. Lunscher. W.H.H.J. and Beddoes. M.P., Optimal edge detector design I: Parameter selection and noise effects,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(2), 164–177 (1986).

    Google Scholar 

  18. Lunscher. W.H.H.J. and Beddoes. M.P. Optimal edge detector design II: Coefficient Quantization,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8(2), 164–177 (1986).

    Google Scholar 

  19. Marr. D. and Hildreth. E., Theory of edge detection,Proc. Royal Society (London) ser.B 207, 187–217 (1980).

    Google Scholar 

  20. Marr, D.,Vision, Freeman, 1982.

  21. Medioni. G.G. and Nevatia. R., Matching images using linear features,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6(6), 675–685 (1984).

    Google Scholar 

  22. Mohammed. J.L., Hummel. R.A. and Zucker. S.W., A gradient projection algorithm for relaxation methods,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-5(3), 330–337 (1983).

    Google Scholar 

  23. Nalwa. V.S. and Binford. T.O., On detecting edges,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 699–714 (1986).

    Google Scholar 

  24. Nevatia, R.,Machine Perception, Prentice-Hall, 1982.

  25. Pavlidis, T.,Algorithms for Graphics and Image Processing, Computer Science Press, 1982.

  26. Prager. J., Extracting and labeling boundary segments in natural scenes,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-2(1), 16–27 (1980).

    Google Scholar 

  27. Preston. K., Duff. M.J.B., Levialdi. S., Norgrem. P.E. and Toriwaki. J.I., Basics of cellular logic with some applications in medical image processing,Proc. IEEE 67(5), 826–856 (1979).

    Google Scholar 

  28. Preston. K., Gray level processing by cellular logic transforms,IEEE Trans. Pattern Anal Mach. Intell. PAMI-5(1), 55–58 (1983).

    Google Scholar 

  29. Preston, K. and Duff, M.J.B.,Modern Cellular Automata: Theory and Applications, Plenmum Press, 1984.

  30. Rosenfeld, A., Relaxation methods in image processing and analysis,Proc. 4th Int. Joint Conf. Pattern Recognition, 1978, pp. 181–185.

  31. Shapira. R., The use of objects' faces in interpreting line drawings,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-6(6), 789–794 (1984).

    Google Scholar 

  32. Shapira. R., More about polyhedra—Interpretation through constructions in the image plane,IEEE Trans. Pattern Anal. Mach. Intell. PAMI-7(1), 1–16 (1985).

    Google Scholar 

  33. Sugihara. K., A necessary and sufficient condition for a picture to represent a polyhedral sceneIEEE Trans. Pattern Anal. Mach. Intell. PAMI-6(5), 578–586 (1984).

    Google Scholar 

  34. Toffoli, T. and Margolus, N.,Cellular Automata Machines: A New Environment for Modeling MIT Press, 1987.

  35. Torre. V. and Poggio. T.A., On edge detection,IEEE. Trans. Pattern Anal. Mach. Intell. PAMI-8(2), 147–163 (1986).

    Google Scholar 

  36. Von Neumann, J., The general logical theory of automata, inCerebral Mechanisms in Behavior—The Hixon Symposium (ed. L.A. Jeffries), Wiley, 1951.

  37. Von Neumann, J.,Theory of Self-Reproducing Automata (ed. A. Burks, Univ. Illinois Press, 1966).

  38. Waltz, D., Understanding line drawings of scenes, with shadows, inThe Psychology of Computer Vision (ed. P.H. Winston), McGraw-Hill, 1975.

  39. Wolfram S. (ed.),Theory and Applications of Cellular Automata, World Scientific, 1986.

  40. Zucker. S.W., Hummel. R.A. and Rosenfeld. A., An application of relaxation labeling to line and curve enhancement,IEEE Trans. Computers C-26(3), 394–403 (1977).

    Google Scholar 

  41. Zucker, S.W. and Parent, P., Multiple-size operators and optimal curve finding, inMultiresolution Image Processing and Analysis (ed. A. Rosenfeld), Springer, 1984.

Download references

Author information

Authors and Affiliations

  1. UPS Research and Development, 51-53 Kenosia Avenue, 06810, Danbury, CT, USA

    Stefan Surka

  2. The Center for Advanced Computer Studies, The University of Southwestern Louisiana, 70504-4330, Lafayette, LA, USA

    Kimon P. Valavanis

Authors
  1. Stefan Surka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kimon P. Valavanis
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Surka, S., Valavanis, K.P. A cellular automata model for edge relaxation. J Intell Robot Syst 4, 379–391 (1991). https://doi.org/10.1007/BF00314941

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00314941

Key words

Search

Navigation