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Proceedings Paper

A SIFT feature based registration algorithm in automatic seal verification
Author(s): Jin He; Xuewen Ding; Hao Zhang; Tiegen Liu
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Paper Abstract

A SIFT (Scale Invariant Feature Transform) feature based registration algorithm is presented to prepare for the seal verification, especially for the verification of high quality counterfeit sample seal. The similarities and the spatial relationships between the matched SIFT features are combined for the registration. SIFT features extracted from the binary model seal and sample seal images are matched according to their similarities. The matching rate is used to define the similar sample seal that is similar with its model seal. For the similar sample seal, the false matches are eliminated according to the position relationship. Then the homography between model seal and sample seal is constructed and named HS . The theoretical homography is namedH . The accuracy of registration is evaluated by the Frobenius norm of H-HS . In experiments, translation, filling and rotation transformations are applied to seals with different shapes, stroke number and structures. After registering the transformed seals and their model seals, the maximum value of the Frobenius norm of their H-HS is not more than 0.03. The results prove that this algorithm can accomplish accurate registration, which is invariant to translation, filling, and rotation transformation, and there is no limit to the seal shapes, stroke number and structures.

Paper Details

Date Published: 30 November 2012
PDF: 10 pages
Proc. SPIE 8558, Optoelectronic Imaging and Multimedia Technology II, 85581R (30 November 2012); doi: 10.1117/12.999384
Show Author Affiliations
Jin He, Tianjin Univ. of Technology and Education (China)
Xuewen Ding, Tianjin Univ. of Technology and Education (China)
Hao Zhang, Tianjin Univ. (China)
Tiegen Liu, Tianjin Univ. (China)

Published in SPIE Proceedings Vol. 8558:
Optoelectronic Imaging and Multimedia Technology II
Tsutomu Shimura; Guangyu Xu; Linmi Tao; Jesse Zheng, Editor(s)

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