A Coupled Relaxation Method for Finding Perceptual Structures

  • Richard C. Wilson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

In this paper we describe a method for determining the existence and parameters of a geometric structure in an image by using a relational representation of its structure. The relational model is matched to image structure in order to find possible instances of the model in the image. Matches between the relational model and image primitives are then used to determine probability distributions for the parameters of a geometric transformation. This transform maps a geometric model of the structure onto an instance in the image. This distribution may then be used to infer a probability map for pixel-based information such as edge responses. When combined with the original edge responses, an enhanced image is produce with more salient edge structure. Iteration of the procedure results in a consistent set of models and edge structure. The method is demonstrated on rectangles undergoing affine transforms.

Keywords

Machine Intelligence Edge Structure Subgraph Isomorphism Relational Graph Edge Response 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full conference paper text

References

  1. 1.
    Kalviainen, H., Hirvonen, P.: An Extention to the Randomized Hough Transform. Pattern Recognition Letters 18(1), 77–85 (1997)CrossRefGoogle Scholar
  2. 2.
    Xu, L., Oja, E., Kultanen, P.: A new curve detection method - Randomized Hough Transform(RHT). Pattern Recognition Letters 11(5), 331–338 (1990)MATHCrossRefGoogle Scholar
  3. 3.
    Xu, L., Oja, E.: Randomized Hough Transform(RHT) - Basic mechanisms, algorithms and computational complexities. CVGIP: Image understanding 57(2), 131–154 (1993)CrossRefGoogle Scholar
  4. 4.
    Bergen, J.R., Shvaytser, H.: A probabilistic algorithm for computing Hough Transforms. Journal of Algorithms 12(4), 639–656 (1991)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Duda, R.O., Hart, P.E.: Pattern Recognition and Scene Analysis. Wiley, New York (1973)Google Scholar
  6. 6.
    Grimson, W.E.L.: On the Recognition of Curved Objects. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(6), 632–643 (1989)CrossRefGoogle Scholar
  7. 7.
    Grimson, W.E.L.: On the Recognition of Parameterized 2D Objects. International Journal of Computer Vision 2(4), 353–372 (1989)CrossRefGoogle Scholar
  8. 8.
    Grimson, W.E.L., Lozano–Pérez, T.: Localizing overlapping parts by searching the interpretation tree. IEEE Transactions on Pattern Analysis and Machine Intelligence 9(4), 469–482 (1987)CrossRefGoogle Scholar
  9. 9.
    Grimson, W.E.L., Lozano–Pérez, T.: Model–Based recognition and localization from sparse range or tactile data. International Journal of Robotics Research 3(3), 3–35 (1984)CrossRefGoogle Scholar
  10. 10.
    Boyer, K., Kak, A.: Structural Stereopsis for 3D vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 10, 144–166 (1998)CrossRefGoogle Scholar
  11. 11.
    Christmas, W., Kittler, J., Petrou, M.: Structural Matching in Computer Vision using Probabilistic Relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 749–764 (1995)CrossRefGoogle Scholar
  12. 12.
    Messmer, B.T., Bunke, H.: Efficient Error-Tolerant Subgraph Isomorphism Detection. Shape, Structure and Pattern Recognition, 231–240 (1994)Google Scholar
  13. 13.
    Sanfeliu, A., Fu, K.S.: A Distance Measure Between Attributed Relational Graphs. IEEE Systems, Man and Cybernetics 13, 353–362 (1983)MATHGoogle Scholar
  14. 14.
    Shapiro, L., Haralick, R.M.: Structural descriptions and inexact matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 3, 504–519 (1981)CrossRefGoogle Scholar
  15. 15.
    Wilson, R.C., Hancock, E.R.: Structural Matching by Discrete Relaxation. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(6), 634–648 (1997)CrossRefGoogle Scholar
  16. 16.
    Kittler, J., Hancock, E.R.: Combining evidence in Probabilistic Relaxation. International Journal of Pattern Recognition and Artificial Intelligence 3, 29–52 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Richard C. Wilson
    • 1
  1. 1.Dept. of Computer ScienceUniversity of YorkUK

Personalised recommendations