Conclusion on substructure matching

In this demo, we show an extension of geometric hashing based on frame features and show that it can be successfully applied to very different problems such as substructure matching of proteins and matching of medical images. The use of frames instead of just points allows to decrease the complexity from at least O(n3) for previous approaches to O(n2), and allows the algorithm to discard several false matches that could not be ruled out just with points. Moreover, it makes the algorithm robust with respect to parameter that tune the quality of the matches (which is the uncertainty that we fix on frames on input).

The drawback of this high selectivity of frames is that the uncertainty handling must be very precise. Several points could be modified in this algorithm to improve robustness and efficiency:

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