Abstract
The Hough Transform is a class of medium-level vision techniques generally recognised as a robust way to detect geometric features from a 2D image. This paper presents two related techniques. First, a new Hough function is proposed based on a Mahalanobis distance measure that incorporates a formal stochastic model for measurement and model noise. Thus, the effects of image and parameter space quantisation can be incorporated directly. Given a resolution of the parameter space, the method provides better results than the Standard Hough Transform (SHT), including under high geometric feature densities. Secondly, Extended Kalman Filtering is used as a further refinement process which achieves not only higher accuracy but also better performance than the SHT. The algorithms are compared with the SHT theoretically and experimentally.
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References
R.O. Duda, P.E. Hart: Use of the Hough transform to detect lines and curves in pictures. Communications of the ACM 15, 11–15 (1972)
A.R. Hare, M.B. Sandler: General test framework for straight-line detection by Hough transforms. IEEE International Symposium on Circuits and Systems, 239–242 (1993)
J. Illingworth, J. Kittler: A survey of the Hough Transform. Computer Vision Graphics and Image Processing 44, 87–116 (1988)
V.F. Leavers: Which Hough transform? IEE Colloquium Digest on Hough Transform, Digest No. 1993/106 (1993)
J. Illingworth, J. Kittler: The adaptive Hough transform. IEEE Transactions on Pattern Analysis & Machine Intelligence 9 (5), 690–697 (1987)
M. Atiquzzaman: Multiresolution Hough transform—An efficient method of detecting patterns in images. IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (11), 1090–1095 (1992)
N. Kiryati, A.M. Bruckstein: What's in a set of points. IEEE Transactions on Pattern Analysis & Machine Intelligence 14 (4), 496–500 (1992)
C.A. Darmon: A recursive method to apply the Hough transform to a set of moving objects. IEEE International Conference on Acoustics, Speech and Signal Processing, 825–829 (1982)
C. Xu: The Mahalanobis Hough Transform with Kalman Filter Refinement. MPhil Transfer Thesis, Internal Report No. 104/SCS/93, Department of Electronic and Electrical Engineering, King's College London, UK (1993)
C. Xu, S. A. Velastin: A Hough transform with integral Kalman filter refinement. IEE Colloquium Digest on Hough Transforms, Digest No. 1993/106, 4/1–4/4 (1993)
C. Xu, S. A. Velastin, A weighted mahalanobis distance Hough transform and its application for the detection of circular segments, IEE Colloquium Digest on Hough Transforms, Digest No. 1993/106, 3/1–3/4 (1993)
N. Ayache, O.D. Faugeras: Maintaining representations of the environment of a mobile robot. IEEE Transactions on Robotics and Automation 5(6), 804–819 (1989)
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© 1994 Springer-Verlag Berlin Heidelberg
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Xu, C., Velastin, S.A. (1994). A comparison between the standard Hough Transform and the Mahalanobis distance Hough Transform. In: Eklundh, JO. (eds) Computer Vision — ECCV '94. ECCV 1994. Lecture Notes in Computer Science, vol 800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57956-7_9
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DOI: https://doi.org/10.1007/3-540-57956-7_9
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