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Volume 32 Issue 10
Dec.  2010
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Zhao Jian, Sun Ji-Xiang, Li Zhi-Yong, Chen Ming-Sheng. Point Pattern Matching Algorithm Based on Relative Shape Context and Spectral Matching Method[J]. Journal of Electronics & Information Technology, 2010, 32(10): 2287-2293. doi: 10.3724/SP.J.1146.2010.00655
Citation: Zhao Jian, Sun Ji-Xiang, Li Zhi-Yong, Chen Ming-Sheng. Point Pattern Matching Algorithm Based on Relative Shape Context and Spectral Matching Method[J]. Journal of Electronics & Information Technology, 2010, 32(10): 2287-2293. doi: 10.3724/SP.J.1146.2010.00655

Point Pattern Matching Algorithm Based on Relative Shape Context and Spectral Matching Method

doi: 10.3724/SP.J.1146.2010.00655
  • Received Date: 2010-06-21
  • Rev Recd Date: 2010-08-20
  • Publish Date: 2010-10-19
  • This paper presents a novel and robust point pattern matching algorithm in which the invariant feature and the method of spectral matching are combined. A new point-set based invariant feature, Relative Shape Context (RSC), is proposed firstly. Using the test statistic of relative shape context descriptors matching scores as the foundation of new compatibility measurement, the assignment graph and the affinity matrix of assignment graph are constructed based on the gained compatibility measurement. Finally, the correct matching results are recovered by using the principal eigenvector of affinity matrix of assignment graph and imposing the mapping constraints required by the overall correspondence mapping. Experiments on both synthetic point-sets and on real world data show that the proposed algorithm is effective and robust.
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