Computing the minimum Hausdorff distance for point sets under translation

Author(s):  
Daniel P. Huttenlocher ◽  
Klara Kedem
2011 ◽  
Vol 412 (32) ◽  
pp. 4173-4186 ◽  
Author(s):  
Christian Knauer ◽  
Maarten Löffler ◽  
Marc Scherfenberg ◽  
Thomas Wolle

2011 ◽  
Vol 21 (03) ◽  
pp. 369-381
Author(s):  
CHRISTIAN KNAUER ◽  
MARC SCHERFENBERG

We present an embedding and search reduction which allow us to build the first data structure for the nearest neighbor search among small point sets with respect to the directed Hausdorff distance under translation. The search structure is non-heuristic in the sense that the quality of the results, the performance, and the space bound are guaranteed. Let n denote the number of point sets in the data base, s the maximal size of a point set, and d the dimension of the points. The nearest neighbor of a query point set under the translation invariant directed Hausdorff distance can be approximated by one or several nearest neighbor searches for single points in the Euclidean embedding space ℝd(s-1). The approximation factor is [Formula: see text] in case of even s and [Formula: see text] when s is odd. Depending on the direction of the Hausdorff distance either the number of queries or the space requirements are exponential in s. Furthermore it is shown how to find the exact nearest neighbor under the directed Hausdorff distance without transformation of the point sets within some weaker time and space bounds.


2017 ◽  
Vol 24 (3) ◽  
pp. 261-277 ◽  
Author(s):  
Dejun Zhang ◽  
Fazhi He ◽  
Soonhung Han ◽  
Lu Zou ◽  
Yiqi Wu ◽  
...  

2017 ◽  
Vol 67 ◽  
pp. 139-148 ◽  
Author(s):  
Yilin Chen ◽  
Fazhi He ◽  
Yiqi Wu ◽  
Neng Hou

Author(s):  
Christian Knauer ◽  
Maarten Löffler ◽  
Marc Scherfenberg ◽  
Thomas Wolle

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