Reliable and Eecient Pattern Matching Using an Aane Invariant Metric

In the eld of pattern matching, there is a clear trade-o between e ectiveness, accuracy and robustness on one hand and e ciency and simplicity on the other hand. For example, matching patterns more e ectively by using a more general class of transformations usually results in a considerable increase of computational complexity. In this paper, we introduce a general pattern matching approach which will be applied to a new measure called the absolute di erence. This patternsimilarity measure is a ne invariant, which stands out favourably in practical use. The problem of nding a transformation mapping to the minimal absolute di erence, like many pattern matching problems, has a high computational complexity. Therefore, we base our algorithm on a hierarchical subdivision of transformation space. The method applies to any a ne group of transformations, allowing optimisations for rigid motion. Our implementation of the method performs well in terms of reliability and e ciency.