| Abstract | We consider various algorithmic solutions to image registration based on thealignment of a set of feature points. We present a number of enhancements
to a branch-and-bound algorithm introduced by Mount, Netanyahu, and Le
Moigne (Pattern Recognition, Vol. 32, 1999, pp. 17–38), which presented a
registration algorithm based on the partial Hausdorff distance. Our enhance-
ments include a new distance measure, the discrete Gaussian mismatch, and
a number of improvements and extensions to the above search algorithm.
Both distance measures are robust to the presence of outliers, that is, data
points from either set that do not match any point of the other set. We
present experimental studies, which show that the new distance measure
considered can provide significant improvements over the partial Hausdorff
distance in instances where the number of outliers is not known in advance.
These experiments also show that our other algorithmic improvements can
offer tangible improvements. We demonstrate the algorithm’s efficacy by
considering images involving different sensors and different spectral bands,
both in a traditional framework and in a multiresolution framework.
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