Congruence, similarity and symmetries of geometric objects
Discrete & Computational Geometry - ACM Symposium on Computational Geometry, Waterloo
Matching points into noise regions: combinatorial bounds and algorithms
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Approximate decision algorithms for point set congruence
Computational Geometry: Theory and Applications
Geometric pattern matching under Euclidean motion
Computational Geometry: Theory and Applications - Special issue: computational geometry, theory and applications
RAPID: randomized pharmacophore identification for drug design
SCG '97 Proceedings of the thirteenth annual symposium on Computational geometry
Multidimensional divide-and-conquer
Communications of the ACM
Approximate congruence in nearly linear time
Computational Geometry: Theory and Applications - Fourth CGC workshop on computional geometry
Point matching under non-uniform distortions
Discrete Applied Mathematics - Special issue: Computational molecular biology series issue IV
Hausdorff distance under translation for points and balls
Proceedings of the nineteenth annual symposium on Computational geometry
The skip quadtree: a simple dynamic data structure for multidimensional data
SCG '05 Proceedings of the twenty-first annual symposium on Computational geometry
Simple algorithms for partial point set pattern matching under rigid motion
Pattern Recognition
GIScience '08 Proceedings of the 5th international conference on Geographic Information Science
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We propose a process for determining approximated matches, in terms of the bottleneck distance, under color preserving rigid motions, between two colored point sets A,B@?R^2, |A|@?|B|. We solve the matching problem by generating all representative motions that bring A close to a subset B^' of set B and then using a graph matching algorithm. We also present an approximate matching algorithm with improved computational time. In order to get better running times for both algorithms we present a lossless filtering preprocessing step. By using it, we determine some candidate zones which are regions that contain a subset S of B such that A may match one or more subsets B^' of S. Then, we solve the matching problem between A and every candidate zone. Experimental results using both synthetic and real data are reported to prove the effectiveness of the proposed approach.