Computing the minimum Hausdorff distance for point sets under translation
SCG '90 Proceedings of the sixth annual symposium on Computational geometry
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International Journal of Computer Vision
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Discrete & Computational Geometry
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Computational Geometry: Theory and Applications
Point set pattern matching in 3-D
Pattern Recognition Letters
Geometric pattern matching under Euclidean motion
Computational Geometry: Theory and Applications - Special issue: computational geometry, theory and applications
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
approximate range searching: the absolute model
WADS'07 Proceedings of the 10th international conference on Algorithms and Data Structures
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We study input sensitive algorithms for point pattern matching under various transformations and the Hausdorff metric as a distance function. Given point sets P and Q in the plane, the problem of point pattern matching is to determine whether P is similar to some portion of Q, where P may undergo transformations from a group G of allowed transformations. All algorithms are based on methods for extracting small subsets from Q that can be matched to a small subset of P. The runtime is proportional to the number k of these subsets. Let d be the number of points in P that are needed to define a transformation in G. The key observation is that for some set B@?P of cardinality larger than d, the number of subsets of Q of this cardinality that match B, is practically small, as the problem becomes more constrained. We present methods to extract efficiently all these subsets in Q. We provide algorithms for homothetic, rigid and similarity transformations in the plane and give a general method that works for any dimension and for any group of transformations. The runtime of our algorithms depends roughly linearly on the number of subsets k, in addition to an nlogn factor. Thus our approximate matching algorithms run roughly in time O(nlogn+kmlogn), where m and n are the number of points in P and Q, respectively. The constants hidden in the big O vary depending on the group of transformations G.