This Article 
 Bibliographic References 
 Add to: 
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
October 2005 (vol. 27 no. 10)
pp. 1523-1535
MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This paper explains the consequences of that omission and describes how the algorithm's theoretical standing and practical performance can be enhanced by deriving estimates of these prior probabilities. Using the priors in guided-MLESAC is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter. This paper describes two further modifications to guided-MLESAC. The first shows how all putative matches, rather than just the best, from a particular feature can be taken forward into the sampling stage, albeit at the expense of additional computation. The second suggests how to propagate the output from one frame forward to successive frames. The additional information makes guided-MLESAC computationally realistic at video-rates for correspondence sets modeled by two transformations and clutter.

[1] M.A. Fischler and R.C. Bolles, “Random Sample Concensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Comm. ACM, vol. 24, no. 6, pp. 381-395, 1981.
[2] P.J. Rousseeuw and A.M. Leroy, Robust Regression and Outlier Detection. New York: Wiley, 1987.
[3] B. Micusik and T. Pajdla, “Using RANSAC for Omnidirectional Camera Model Fitting,” Proc. Eighth Computer Vision Winter Workshop, Feb. 2003.
[4] T. Okabe and Y. Sato, “Object Recognition Based on Photometric Alignment Using RANSAC,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 221-228, June 2003.
[5] P.H.S. Torr and D.W. Murray, “Statistical Detection of Independent Movement from a Moving Camera,” Image and Vision Computing, vol. 11, no. 4, pp. 180-187, 1993.
[6] L. Shapiro, Affine Analysis of Image Sequences. Cambridge, U.K.: Cambridge Univ. Press, 1995.
[7] G. Xu and Z. Zhang, Epipolar Geometry in Stereo, Motion and Object Recognition. Kluwer Academic, 1996.
[8] P.H.S. Torr and D.W. Murray, “The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix,” Int'l J. Computer Vision, vol. 24, no. 3, pp. 271-300, Sept. 1997.
[9] P.H.S. Torr and A. Zisserman, “MLESAC: A New Robust Estimator with Application to Estimating Image Geometry,” Computer Vision and Image Understanding, vol. 78, pp. 138-156, 2000.
[10] P.J. Huber, Robust Statistics. John Wiley and Sons, 1985.
[11] Q.-T. Luong and T. Viéville, “Canonical Representations for the Geometries of Multiple Projective Views,” Computer Vision and Image Understanding, vol. 64, no. 2, pp. 193-229, 1996.
[12] B.J. Tordoff, “Controlling Zoom in Active Vision,” PhD thesis, Univ. of Oxford, 2002.
[13] P.H.S. Torr, A. Zisserman, and S. Maybank, “Robust Detection of Degenerate Configurations while Estimating the Fundamental Matrix,” Computer Vision and Image Understanding, vol. 71, no. 3, pp. 312-333, 1998.
[14] O. Chum, J. Matas, and J. Kittler, “Locally Optimized RANSAC,” Proc. 25th DAGM Symp. Pattern Recognition, B. Michaelis and G. Krell, eds., pp. 236-243, 2003.
[15] O. Chum, J. Matas, and S. Obdrzalek, “Enhancing RANSAC by Generalized Model Optimization,” Proc. Asian Conf. Computer Vision, 2004.
[16] R.I. Hartley, “Self-Calibration from Multiple Views with a Rotating Camera,” Proc. Third European Conf. Computer Vision, pp. A:471-478, 1994.
[17] L. de Agapito, R.I. Hartley, and E. Hayman, “Linear Self-Calibration of a Rotating and Zooming Camera,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 15-21, 1999.

Index Terms:
Index Terms- Random sampling, correspondence, image transformation, maximum-likelihood estimation.
Ben J. Tordoff, David W. Murray, "Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1523-1535, Oct. 2005, doi:10.1109/TPAMI.2005.199
Usage of this product signifies your acceptance of the Terms of Use.