Efficient algorithms for robust feature matching

David M. Mount, Nathan S. Netanyahu, Jacqueline Le Moigne

Research output: Contribution to journalArticlepeer-review

138 Scopus citations


One of the basic building blocks in any point-based registration scheme involves matching feature points that are extracted from a sensed image to their counterparts in a reference image. This leads to the fundamental problem of point matching: Given two sets of points, find the (affine) transformation that transforms one point set so that its distance from the other point set is minimized. Because of measurement errors and the presence of outlying data points, it is important that the distance measure between the two point sets be robust to these effects. We measure distances using the partial Hausdorff distance. Point matching can be a computationally intensive task, and a number of theoretical and applied approaches have been proposed for solving this problem. In this paper, we present two algorithmic approaches to the point matching problem, in an attempt to reduce its computational complexity, while still providing a guarantee of the quality of the final match. Our first method is an approximation algorithm, which is loosely based on a branch-and-bound approach due to Huttenlocher and Rucklidge, (Technical Report 1321, Dept. of Computer Science, Cornell University, Ithaca, 1992; Proc. IEEE Conf. on Computer vision and Pattern Recognition, New York, 1993, pp. 705-706). We show that by varying the approximation error bounds, it is possible to achieve a tradeoff between the quality of the match and the running time of the algorithm. Our second method involves a Monte Carlo method for accelerating the search process used in the first algorithm. This algorithm operates within the framework of a branch-and-bound procedure, but employs point-to-point alignments to accelerate the search. We show that this combination retains many of the strengths of branch-and-bound search, but provides significantly faster search times by exploiting alignments. With high probability, this method succeeds in finding an approximately optimal match. We demonstrate the algorithms' performances on both synthetically generated data points and actual satellite images.

Original languageEnglish
Pages (from-to)17-38
Number of pages22
JournalPattern Recognition
Issue number1
StatePublished - Jan 1999
Externally publishedYes

Bibliographical note

Funding Information:
1 A preliminary version of this paper appeared in Proc. CESDIS Image Registration ¼orkshop, NASA Goddard Space Flight Center (GSFC), Greenbelt, MD, 1997, and NASA Publication CP-1998-206853, pp. 247—256. 2 E-mail: mount@cs.umd.edu. The support of the National Science Foundation under grant CCR-9712379 is gratefully acknowledged. 3E-mail: lemoigne@cesdis.gsfc.nasa.gov. The support of the AISB, Code 935, NASA/GSFC is gratefully acknowledged. * Corresponding author. E-mail: nathan@cfar.umd.edu. This research was carried out, in part, while the author was also affiliated with the Center of Excellence in Space Data and Information Sciences (CESDIS) Code 930.5, NASA/ GSFC. The support of the Applied Information Sciences Branch (AISB), Code 935, NASA/GSFC, under contract NAS 5555-37 and grant NAG5-6699 is gratefully acknowledged.


  • Approximation algorithms
  • Hausdorff distance
  • Image registration
  • Point pattern matching


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