This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Maximum-Likelihood Image Matching
June 2002 (vol. 24 no. 6)
pp. 853-857

Image matching applications such as tracking and stereo commonly use the sum-of-squared-difference (SSD) measure to determine the best match. However, this measure is sensitive to outliers and is not robust to template variations. Alternative measures have also been proposed that are more robust to these issues. We improve upon these using a probabilistic formulation for image matching in terms of maximum-likelihood estimation that can be used for both edge template matching and gray-level image matching. This formulation generalizes previous edge matching methods based on distance transforms. We apply the techniques to stereo matching and feature tracking. Uncertainty estimation techniques allow feature selection to be performed by choosing features that minimize the localization uncertainty.

[1] M. Black and P. Anandan, The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields J. Computer Vision and Image Understanding, vol. 63, no. 1, pp. 75-104, 1996.
[2] M.J. Black and A. Rangarajan, “On the Unification of Line Processes, Outlier Rejection, and Robust Statistics with Applications in Early Vision,” Int'l J. Computer Vision, vol. 19, no. 1, pp. 57-91, 1996.
[3] C.F. Olson, “Maximum-Likelihood Template Matching,” Proc. Computer Vision and Pattern Recognition, 2000.
[4] D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge, “Comparing Images Using the Hausdorff Distance,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, Sept. 1993.
[5] D. Huttenlocher, R. Lilien, and C. Olson, “View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 951-955, Sept. 1999.
[6] C.F. Olson, Probabilistic Self-Localization for Mobile Robots IEEE Trans. Robotics and Automation, vol. 16, no. 1, pp. 55-66, Feb. 2000.
[7] A. Rosenfeld and J. Pfaltz,“Sequential operations in digital picture processing,” J. ACM, vol. 13, pp. 471-494, 1966.
[8] G. Borgefors, “Distance Transforms in Digital Images,” Computer Vision, Graphics, and Image Processing, vol. 34, pp. 344-371, 1986.
[9] D.W. Pagliero,“Distance transforms,” Computer Vision, Graphics, and Image Processing: Graphical models and Image Processing, vol. 54, pp. 56-74, 1992.
[10] D.P. Huttenlocher, “Using Two-Dimensional Models to Interact with the Three-Dimensional World,” Proc. Int'l NSF-ARPA Workshop Object Representation in Computer Vision, pp. 109-124, 1995.
[11] D.P. Huttenlocher and W.J. Rucklidge, “A Multi-Resolution Technique for Comparing Images Using the Hausdorff Distance,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 705-706, 1993.
[12] W. Rucklidge, “Efficiently Locating Objects Using the Hausdorff Distance,” Int'l J. Computer Vision, vol. 24, no. 3, pp. 251–270, 1997.
[13] C.F. Olson, “Landmark Selection for Terrain Matching,” Proc. Int'l Conf. Robotics and Automation, pp. 1447-1452, 2000.
[14] M.J. Black, D.J. Fleet, and Y. Yacoob, “Robustly Estimating Changes in Image Appearance,” Computer Vision and Image Understanding, vol. 78, no. 1, pp. 8–31, 2000.
[15] P. Viola and W.M. WellsIII, “Alignment by Maximization of Mutual Information,” Int'l J. Computer Vision, vol. 24, no. 2, pp. 137-154, 1997.
[16] W. Rucklidge, Efficient Guaranteed Search for Gray-Level Patterns Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 717-723, 1997.

Index Terms:
Image matching, tracking, stereo, maximum-likelihood estimation.
Citation:
Clark F. Olson, "Maximum-Likelihood Image Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 853-857, June 2002, doi:10.1109/TPAMI.2002.1008392
Usage of this product signifies your acceptance of the Terms of Use.