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On the Sensitivity of the Hough Transform for Object Recognition
March 1990 (vol. 12 no. 3)
pp. 255-274

Object recognition from sensory data involves, in part, determining the pose of a model with respect to a scene. A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space whose axes are the quantized transformation parameters. Large clusters of similar transformations in that space are taken as evidence of a correct match. A theoretical analysis of the behavior of such methods is presented. The authors derive bounds on the set of transformations consistent with each pairing of data and model features, in the presence of noise and occlusion in the image. Bounds are provided on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. It is argued that haphazardly applying such methods to complex recognition tasks is risky, as the probability of false positives can be very high.

[1] V. S. Alagar and L. H. Thiel, "Algorithms for detectingM-dimensional objects inN-dimensional spaces,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, no. 3, pp. 245-256, 1981.
[2] D. H. Ballard, "Generalizing the Hough transform to detect arbitrary shapes,"Pattern Recogn., vol. 13, p. 111, 1981.
[3] P. J. Besl and R. C. Jain, "Three-dimensional object recognition,"ACM Comput. Surveys, vol. 17, no. 1, pp. 75-145, Mar. 1985.
[4] C. M. Brown, "Inherent bias and noise in the Hough transform,"IEEE Trans. Pattern Anal. Machine Intell., vol. 5, no. 5, pp. 493- 505, 1983.
[5] R.T. Chin and C. R. Dyer, "Model-based recognition in robot vision,"ACM Comput. Surveys, vol. 18, no. 1, pp. 67-108, Mar. 1986.
[6] D. T. Clemens, "The recognition of two-dimensional modeled objects in images," M.S. thesis, Dep. Elec. Eng. Comput. Sci., Massachusetts Inst. Technol., 1986.
[7] M. Cohen and G. T. Toussaint, "On the detection of structures in noisy pictures,"Pattern Recogn., vol. 9, pp. 95-98, 1977.
[8] L. S. Davis, "Hierarchical generalized Hough transforms and linesegment based generalized Hough transforms,"Pattern Recogn., vol. 15, p. 277, 1982.
[9] W. Feller,An Introduction to Probability Theory and Its Applications. New York: Wiley, 1968.
[10] Y. Lamdan, J. T. Schwartz, and H. J. Wolfson, "On recognition of 3-D objects from 2-D images," Courant Inst., New York Univ., Robotics Rep. 122, 1987.
[11] S. Linainmaa, D. Harwood, and L. S. Davis, "Pose determination of a three-dimensional object using triangle pairs," Center Automation Res., Univ. Maryland, Rep. CAR-TR-143, 1985.
[12] H. Maitre, "Contribution to the prediction of performances of the Hough transform,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, no. 5, pp. 669-674, Sept. 1986.
[13] S. D. Shapiro, "Transformations for the computer detection of curves in noisy pictures,"Comput. Graphics Image Processing, vol. 4, pp. 328-338, 1975.
[14] S. D. Shapiro and A. Iannino, "Geometric constructions for predicting Hough transform performance,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-1, no. 3, pp. 310-317, 1979.
[15] T. M. Silberberg, L. S. Davis, and D. A. Harwood, "An iterative Hough procedure for three-dimensional object recognition,"Pattern Recogn., vol. 17, no. 6, pp. 612-629, 1984.
[16] T. Silberberg, D. Harwood, and L. Davis, "Object recognition using oriented model points,"Comput. vision, Graphics, ImageProcessing, vol. 35, pp. 47-71, 1986.
[17] G. Stockman, S. Kopstein, and S. Benett, "Matching images to models for registration and object detection via clustering,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-3, pp. 229-241, 1982.
[18] D. Thompson and J. Mundy, "Three dimensional model matching from an unconstrained viewpoint,"Proc. Int. Conf. Robotics Automation, 1987, pp. 208-220.
[19] J. L. Turney, T. N. Mudge, and R. A. Volz, "Recognizing partially occluded parts,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-7, no. 4, pp. 410-421, 1985.
[20] T. M. van Veen and F. C. A. Groen, "Discretization errors in the Hough transform,"Pattern Recogn., vol. 14, pp. 137-145, 1981.

Index Terms:
pattern recognition; picture processing; false peak likelihood bound; sensitivity; Hough transform; object recognition; sensory data; coordinate transformations; noise; occlusion; tessellation effects; false positives; pattern recognition; picture processing; sensitivity; transforms
Citation:
W.E.L. Grimson, D.P. Huttenlocher, "On the Sensitivity of the Hough Transform for Object Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 3, pp. 255-274, March 1990, doi:10.1109/34.49052
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