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SSPR
1998
Springer

Object Recognition from Large Structural Libraries

11 years 5 months ago
Object Recognition from Large Structural Libraries
This paper presents a probabilistic similarity measure for object recognition from large libraries of line-patterns. We commence from a structural pattern representation which uses a nearest neighbour graph to establish the adjacency of line-segments. Associated with each pair of line-segments connected in this way is a vector of Euclidean invariant relative angle and distance ratio attributes. The relational similarity measure uses robust error kernels to compare sets of pairwise attributes on the edges of a nearest neighbour graph. We use the relational similarity measure in a series of recognition experiments which involve a library of over 2500 line-patterns. A sensitivity study reveals that the method is capable of delivering a recognition accuracy of 94%. A comparative study reveals that the method is most e ective when either a Gaussian kernel or Huber's robust kernel is used to weight the attribute relations. Moreover, the method consistently outperforms the standard and ...
Benoit Huet, Edwin R. Hancock
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where SSPR
Authors Benoit Huet, Edwin R. Hancock
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