Skip to main content
SpringerLink
Log in

A General Method for Geometric Feature Matching and Model Extraction

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

Popular algorithms for feature matching and model extraction fall into two broad categories: generate-and-test and Hough transform variations. However, both methods suffer from problems in practical implementations. Generate-and-test methods are sensitive to noise in the data. They often fail when the generated model fit is poor due to error in the data used to generate the model position. Hough transform variations are less sensitive to noise, but implementations for complex problems suffer from large time and space requirements and from the detection of false positives. This paper describes a general method for solving problems where a model is extracted from, or fit to, data that draws benefits from both generate-and-test methods and those based on the Hough transform, yielding a method superior to both. An important component of the method is the subdivision of the problem into many subproblems. This allows efficient generate-and-test techniques to be used, including the use of randomization to limit the number of subproblems that must be examined. Each subproblem is solved using pose space analysis techniques similar to the Hough transform, which lowers the sensitivity of the method to noise. This strategy is easy to implement and results in practical algorithms that are efficient and robust. We describe case studies of the application of this method to object recognition, geometric primitive extraction, robust regression, and motion segmentation.

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

  • Alter, T.D. and Jacobs, D.W. 1998. Uncertainty propagation in model-based recognition. International Journal of Computer Vision, 27(2):127-159.

    Google Scholar 

  • Ayache, N. and Faugeras, O.D. 1986. HYPER: A new approach for the recognition and positioning of two-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(1):44-54.

    Google Scholar 

  • Ballard, D.H. 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13(2):111-122.

    Google Scholar 

  • Bergen, J.R. and Shvaytser, H. 1991. A probabilistic algorithm for computing Hough transforms. Journal of Algorithms, 12:639-656.

    Google Scholar 

  • Bolles, R.C. and Fischler, M.S. 1981. ARANSAC-based approach to model fitting and its application to finding cylinders in range data. In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 637-642.

  • Breuel, T.M. 1992. Fast recognition using adaptive subdivisions of transformation space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 445-451.

  • Cass, T.A. 1997. Polynomial-time geometric matching for object recognition. International Journal of Computer Vision, 21(1/2):37-61.

    Google Scholar 

  • Duda, R.O. and Hart, P.E. 1972. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 15:11-15.

    Google Scholar 

  • Edelsbrunner, H. and Souvaine, D.L. 1990. Computing least median of squares regression lines and guided topological sweep. Journal of the American Statistical Association, 85(409):115-119.

    Google Scholar 

  • Faugeras, O.D. and Hebert, M. 1986. The representation, recognition, and locating of 3-d objects. International Journal of Robotics Research, 5(3):27-52.

    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, 24:381-396.

    Google Scholar 

  • Grimson, W.E.L. and Huttenlocher, D.P. 1990. On the sensitivity of the Hough transform for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(3):255-274.

    Google Scholar 

  • Grimson, W.E.L., Huttenlocher, D.P., and Jacobs, D.W. 1994. A study of affine matching with bounded sensor error. International Journal of Computer Vision, 13(1):7-32.

    Google Scholar 

  • Hough, P.V.C. 1962. Method and means for recognizing complex patterns. U.S. Patent 3069654.

  • Huang, T.S. and Netravali, A.N. 1994. Motion and structure from feature correspondences: A review. In Proceedings of the IEEE, 82(2):252-268.

    Google Scholar 

  • Huttenlocher, D.P. and Ullman, S. 1990. Recognizing solid objects by alignment with an image. International Journal of Computer Vision, 5(2):195-212.

    Google Scholar 

  • Illingworth, J. and Kittler, J. 1988. A survey of the Hough transform. Computer Vision, Graphics, and Image Processing, 44:87-116.

    Google Scholar 

  • Jacobs, D.W. 1996. Robust and efficient detection of salient convex groups. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(1):23-37.

    Google Scholar 

  • Kiryati, N., Eldar, Y., and Bruckstein, A.M. 1991. A probabilistic Hough transform. Pattern Recognition, 24(4):303-316.

    Google Scholar 

  • Leavers, V.F. 1992. The dynamic generalized Hough transform: Its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses. CVGIP: Image Understanding, 56(3):381-398.

    Google Scholar 

  • Leavers, V.F. 1993. Which Hough transform? CVGIP: Image Understanding, 58(2):250-264.

    Google Scholar 

  • Linnainmaa, S., Harwood, D., and Davis, L.S. 1988. Pose determination of a three-dimensional object using triangle pairs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(5):634-647.

    Google Scholar 

  • Lowe, D.G. 1985. Perceptual Organization and Visual Recognition. Kluwer Academic: Dordrecht.

    Google Scholar 

  • Lowe, D.G. 1987. Three-dimensional object recognition from single two-dimensional images. Artificial Intelligence, Boston, MA, 31:355-395.

    Google Scholar 

  • Murakami, K., Koshimizu, H., and Hasegawa, K. 1986. On the new Hough algorithms without two-dimensional array for parameter space to detect a set of straight lines. In Proceedings of the IAPR International Conference on Pattern Recognition, pp. 831-833.

  • Olson, C.F. 1997a. An approximation algorithm for least median of squares regression. Information Processing Letters, 63(5):237-241.

    Google Scholar 

  • Olson, C.F. 1997b. Efficient pose clustering using a randomized algorithm. International Journal of Computer Vision, 23(2):131-147.

    Google Scholar 

  • Olson, C.F. 1998. Improving the generalized Hough transform through imperfect grouping. Image and Vision Computing, 16(9/10):627-634.

    Google Scholar 

  • Olson, C.F. 1999. Constrained Hough transforms for curve detection. Computer Vision and Image Understanding, 73(3):329-345.

    Google Scholar 

  • Olson, C.F. 2000. A general method for feature matching and model extraction. Vision Algorithms: Theory and Practice, LNCS 1883, Springer-Verlag: Berlin, pp. 20-36.

    Google Scholar 

  • Rosenfeld, A. 1969. Picture Processing by Computer. Academic Press: New York, NY.

    Google Scholar 

  • Rousseeuw, P.J. and Leroy, A.M. 1987. Robust Regression and Outlier Detection. John Wiley and Sons: New York.

    Google Scholar 

  • Shapiro, S.D. 1978. Generalization of the Hough transform for curve detection in noisy digital images. In Proceedings of the International Joint Conference on Pattern Recognition, pp. 710-714.

  • Silberberg, T.M., Davis, L.S., and Harwood, D.A. 1984. An iterative Hough procedure for three-dimensional object recognition. Pattern Recognition, 17(6):621-629.

    Google Scholar 

  • Stockman, G. 1987. Object recognition and localization via pose clustering. Computer Vision, Graphics, and Image Processing, 40:361-387.

    Google Scholar 

  • Thompson, D.W. and Mundy, J.L. 1987. Three-dimensional model matching from an unconstrained viewpoint. In Proceedings of the IEEE Conference on Robotics and Automation, Vol. 1, pp. 208-220.

    Google Scholar 

  • Xu, L., Oja, E., and Kultanen, P. 1990. A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Letters, 11:331-338.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computing and Software Systems, University of Washington, Bothell, 18115 Campus Way NE, Box 358534, Bothell, WA, 98011-8246, USA

    Clark F. Olson

Authors
  1. Clark F. Olson
    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

Olson, C.F. A General Method for Geometric Feature Matching and Model Extraction. International Journal of Computer Vision 45, 39–54 (2001). https://doi.org/10.1023/A:1012317923177

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1012317923177

Search

Navigation