ROR: Rejection of Outliers By Rotations

Amit Adam, Ehud Rivlin, and Ilan Shimshoni.
ROR: Rejection of Outliers by Rotations.
PAMI, 23(1):78-84, 2001

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Abstract

We address the problem of rejecting false matches of points between two perspective views. The two views are taken from two arbitrary, unknown positions and orientations. Even the best algorithms for image matching make some mistakes and output some false matches. We present an algorithm for identification of the false matches between the views. The algorithm exploits the possibility of rotating one of the images to achieve some common behavior of the correct matches. Those matches that deviate from this common behavior turn out to be false matches. Our algorithm does not, in any way, use the image characteristics of the matched features. In particular, it avoids problems that cause the false matches in the first place. The algorithm works even in cases where the percentage of false matches is as high as 85 percent. The algorithm may be run as a postprocessing step on output from any point matching algorithm. Use of the algorithm may significantly improve the ratio of correct matches to incorrect matches. For robust estimation algorithms which are later employed, this is a very desirable quality since it reduces significantly their computational cost. We present the algorithm, identify the conditions under which it works, and present results of testing it on both synthetic and real images. The code for the algorithm is available through the World Wide Web.

Keywords

Co-authors

Bibtex Entry

@article{AdamRS01a,
  title = {ROR: Rejection of Outliers by Rotations},
  author = {Amit Adam and Ehud Rivlin and Ilan Shimshoni},
  year = {2001},
  month = {January},
  journal = {PAMI},
  volume = {23},
  number = {1},
  pages = {78-84},
  keywords = {Correspondence problem, feature matching, false matches, outliers, outlier rejection, robust estimation.},
  abstract = {We address the problem of rejecting false matches of points between two perspective views. The two views are taken from two arbitrary, unknown positions and orientations. Even the best algorithms for image matching make some mistakes and output some false matches. We present an algorithm for identification of the false matches between the views. The algorithm exploits the possibility of rotating one of the images to achieve some common behavior of the correct matches. Those matches that deviate from this common behavior turn out to be false matches. Our algorithm does not, in any way, use the image characteristics of the matched features. In particular, it avoids problems that cause the false matches in the first place. The algorithm works even in cases where the percentage of false matches is as high as 85 percent. The algorithm may be run as a postprocessing step on output from any point matching algorithm. Use of the algorithm may significantly improve the ratio of correct matches to incorrect matches. For robust estimation algorithms which are later employed, this is a very desirable quality since it reduces significantly their computational cost. We present the algorithm, identify the conditions under which it works, and present results of testing it on both synthetic and real images. The code for the algorithm is available through the World Wide Web.}
}