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Reconstruction of Banknote Fragments Based on Keypoint Matching Method
Author(s) -
Gwo ChihYing,
Wei ChiaHung,
Li Yue,
Chiu NanHsing
Publication year - 2015
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/1556-4029.12777
Subject(s) - banknote , scale invariant feature transform , artificial intelligence , similarity (geometry) , pattern recognition (psychology) , computer vision , computer science , fragment (logic) , matching (statistics) , orientation (vector space) , scale (ratio) , mathematics , image (mathematics) , algorithm , geometry , statistics , physics , quantum mechanics
Banknotes may be shredded by a scrap machine, ripped up by hand, or damaged in accidents. This study proposes an image registration method for reconstruction of multiple sheets of banknotes. The proposed method first constructs different scale spaces to identify keypoints in the underlying banknote fragments. Next, the features of those keypoints are extracted to represent their local patterns around keypoints. Then, similarity is computed to find the keypoint pairs between the fragment and the reference banknote. The banknote fragments can determine the coordinate and amend the orientation. Finally, an assembly strategy is proposed to piece multiple sheets of banknote fragments together. Experimental results show that the proposed method causes, on average, a deviation of 0.12457 ± 0.12810° for each fragment while the SIFT method deviates 1.16893 ± 2.35254° on average. The proposed method not only reconstructs the banknotes but also decreases the computing cost. Furthermore, the proposed method can estimate relatively precisely the orientation of the banknote fragments to assemble.