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 selected features. Hough transform variations are somewhat less sensitive the noise, but implementations for complex problems suffer from large time and space requirements and 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. However, the subproblems are 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 apply 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 chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
T. D. Alter and D. W. Jacobs. Uncertainty propagation in model-based recognition. International Journal of Computer Vision, 27(2):127–159, 1998.
J. R. Bergen and H. Shvaytser. A probabilistic algorithm for computing Hough transforms. Journal of Algorithms, 12:639–656, 1991.
T. M. Breuel. Fast recognition using adaptive subdivisions of transformation space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 445–451, 1992.
T. A. Cass. Polynomial-time geometric matching for object recognition. International Journal of Computer Vision, 21(1/2):37–61, January 1997.
H. Edelsbrunner and D. L. Souvaine. Computing least median of squares regression lines and guided topological sweep. Journal of the American Statistical Association, 85(409):115–119, March 1990.
M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24:381–396, June 1981.
W. E. L. Grimson and D. P. Huttenlocher. On the sensitivity of the Hough transform for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(3):255–274, March 1990.
W. E. L. Grimson, D. P. Huttenlocher, and D. W. Jacobs. A study of affine matching with bounded sensor error. International Journal of Computer Vision, 13(1):7–32, 1994.
T. S. Huang and A. N. Netravali. Motion and structure from feature correspondences: A review. Proceedings of the IEEE, 82(2):252–268, February 1994.
D. P. Huttenlocher and S. Ullman. Recognizing solid objects by alignment with an image. International Journal of Computer Vision, 5(2):195–212, 1990.
J. Illingworth and J. Kittler. A survey of the Hough transform. Computer Vision, Graphics, and Image Processing, 44:87–116, 1988.
D. W. Jacobs. Robust and efficient detection of salient convex groups. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(1):23–37, 1996.
N. Kiryati, Y. Eldar, and A. M. Bruckstein. A probabilistic Hough transform. Pattern Recognition, 24(4):303–316, 1991.
V. F. Leavers. 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, November 1992.
V. F. Leavers. Which Hough transform? CVGIP: Image Understanding, 58(2):250–264, September 1993.
R. J. Lipton and R. E. Tarjan. Applications of a planar separator theorem. SIAM Journal on Computing, 9(3):615–627, 1980.
D. G. Lowe. Perceptual Organization and Visual Recognition. Kluwer, 1985.
K. Murakami, H. Koshimizu, and K. Hasegawa. On the new Hough algorithms without two-dimensional array for parameter space to detect a set of straight lines. In Proceedings of the I APR International Conference on Pattern Recognition, pages 831–833, 1986.
C. F. Olson. An approximation algorithm for least median of squares regression. Information Processing Letters, 63(5):237–241, September 1997.
C. F. Olson. Efficient pose clustering using a randomized algorithm. International Journal of Computer Vision, 23(2):131–147, June 1997.
C. F. Olson. Improving the generalized Hough transform through imperfect grouping. Image and Vision Computing, 16(9–10):627–634, July 1998.
C. F. Olson. Constrained Hough transforms for curve detection. Computer Vision and Image Understanding, 73(3):329–345, March 1999.
P. J. Rousseeuw and A. M. Leroy. Robust Regression and Outlier Detection. John Wiley and Sons, 1987.
L. Xu, E. Oja, and P. Kultanen. A new curve detection method: Randomized Hough transform (RHT). Pattern Recognition Letters, 11:331–338, May 1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Olson, C.F. (2000). A General Method for Feature Matching and Model Extraction. In: Triggs, B., Zisserman, A., Szeliski, R. (eds) Vision Algorithms: Theory and Practice. IWVA 1999. Lecture Notes in Computer Science, vol 1883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44480-7_2
Download citation
DOI: https://doi.org/10.1007/3-540-44480-7_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67973-8
Online ISBN: 978-3-540-44480-0
eBook Packages: Springer Book Archive