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A Deformable Template Approach to Detecting Straight Edges in Radar Images
April 1996 (vol. 18 no. 4)
pp. 438-443

Abstract—This paper addresses the problem of locating two straight and parallel road edges in images that are acquired from a stationary millimeter-wave radar platform positioned near ground-level. A fast, robust, and completely data-driven Bayesian solution to this problem is developed, and it has applications in automotive vision enhancement. The method employed in this paper makes use of a deformable template model of the expected road edges, a two-parameter log-normal model of the ground-level millimeter-wave (GLEM) radar imaging process, a maximum a posteriori (MAP) formulation of the straight edge detection problem, and a Monte Carlo algorithm to maximize the posterior density. Experimental results are presented by applying the method on GLEM radar images of actual roads. The performance of the method is assessed against ground truth for a variety of road scenes.

[1] J. Aitchison and J.A.C. Brown, The Lognormal Distribution. Cambridge Univ. Press, 1957.
[2] Y. Amit, U. Grenander, and M. Piccioni, "Structural image restoration through deformable templates," J. Am. Statististical Assoc., vol. 86, pp. 376-387, 1991.
[3] A. Blake and A. Zisserman, Visual Reconstruction. MIT Press, 1987.
[4] Markov Random Fields: Theory and Applications, R. Chellappa and A.K. Jain, eds. Academic Press, 1993.
[5] Y.S. Chow, U. Grenander, and D.M. Keenan, HANDS. A Pattern-Theoretic Study Of Biological Shapes.New York: Springer-Verlag, 1991.
[6] A. Farina, A. Russo, and F.A. Studer, "Coherent radar detection in log-normal clutter," IEE Proc.—Comm., Radar, and Signal Process., Part F, vol. 133, pp. 39-54, 1986.
[7] R. Kasturi and R.C. Jain, Computer Vision: Principles. IEEE CS Press, 1991.
[8] S. Lakshmanan and H. Derin, “Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, pp. 799-813, 1989.
[9] S. Lakshmanan and K.C. Kluge, "Lane detection for automotive sensors," Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 1995.
[10] J.-S. Lee, "Speckle analysis and smoothing of synthetic aperture radar images," Computer Vision Graphics Image Processing, vol. 17, pp. 24-32, 1981.
[11] N. Metropolis, A.W. Rosenbluth, A.H. Teller, and E. Teller, "Equations of state calculations by fast computing machines," J. Chemistry and Physics, vol. 21, pp. 1,087-1,091, 1953.
[12] M.L. Skolnik, Introduction to Radar Systems. McGraw-Hill, 1980.
[13] D. Terzopoulos, "Regularization of Inverse Visual Problems Involving Discontinuities," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 4, pp. 413-424, 1986.
[14] J. Scheer, G. Ewell, R. Kerr, S. Piper, M. Belcher, J. Echard, M. Richards, E. Sjoberg, J. Kurtz, T. Lane, J. Bruder, and T. Wallace, Coherent Radar Performance Estimation—Course Notes, Georgia Tech. Research Inst., 1992.

Index Terms:
Road boundary detection, millimeter-wave images, global shape models, log-normal densities, Metropolis algorithm.
Sridhar Lakshmanan, David Grimmer, "A Deformable Template Approach to Detecting Straight Edges in Radar Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 4, pp. 438-443, April 1996, doi:10.1109/34.491625
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