Acta Optica Sinica, Volume. 33, Issue 12, 1215001(2013)
Point Pattern Matching Based on Multiple Spectral Representations
Addressing the weakness of the single spectral representation, an algorithm is proposed for point pattern matching on the basis of multiple spectral representations. The eigenvalue series obtained by various matrix representations of graphs are used as the descriptor of feature point. The similarities between the given local structural descriptors are evaluated via the technique of multiview spectral embedding. Combined with the geometric consistency, point pattern matching problem is solved by using the method of probabilistic relaxation. Comparative experiments conducted on both synthetic data and real images verify the effectiveness and robustness of the proposed method.
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Tang Jun, Liu Zhizhong, Liang Dong, Wang Nian. Point Pattern Matching Based on Multiple Spectral Representations[J]. Acta Optica Sinica, 2013, 33(12): 1215001
Category: Machine Vision
Received: May. 26, 2013
Accepted: --
Published Online: Dec. 5, 2013
The Author Email: Jun Tang (tangjunahu@163.com)