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New banknote defect detection algorithm using quaternion wavelet transform
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AI
DOI：10.1016/j.neucom.2015.12.112.png)
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摘要
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In order to improve the accuracy of detection of defects in banknote sorting, a new algorithm is proposed to detect cracks and scratches on banknote images. The quaternion wavelet transform and the least squares method are used for the banknote image registration. The features of the defects that are robust to gray intensity changes are constructed using edge information captured by the Kirsch operator. The banknote image is divided into several subzones of fixed size. The level of defect of the banknote image is estimated based on the defective features of each sub-zone. The experimental results show that the proposed algorithm is robust even with low quality banknote images and can obtain a high recognition rate and high stability. The proposed algorithm has already been used in a practical banknote sorting system. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Quaternion wavelet transform
Feature extraction
Banknote classification
Defect detection
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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