Analysis and Recognition of Asian Scripts - the State of the Art
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Comparing elastic alignment algorithms for the off-line signature verification problem
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
A novel fusing algorithm for retinal fundus images
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Mosaicing the retinal fundus images: a robust registration technique based approach
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Similarity measurement for off-line signature verification
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
A global-to-local matching strategy for registering retinal fundus images
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
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Two patterns are matched by putting one on top of the other and iteratively moving their individual parts until most of their corresponding parts are aligned. An energy function and a neighborhood of influence are defined for each iteration. Initially, a large neighborhood is used such that the movements result in global features being coarsely aligned. The neighborhood size is gradually reduced in successive iterations so that finer and finer details are aligned. Encouraging results have been obtained when applied to match complex Chinese characters. It has been observed that computation increases with the square of the number of moving parts which is quite favorable compared with other algorithms. The method was applied to the recognition of handwritten Chinese characters. After performing the iterative matching, a set of similarity measures are used to measure the similarity in topological features between the input and template characters. An overall recognition rate of 96.1% is achieved