
Proceedings Paper
Improvement of authenticity inspection accuracy using logo region detectionFormat | Member Price | Non-Member Price |
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Paper Abstract
Manufacturing technology for counterfeit brand items has advanced in recent years, and it is becoming very difficult for humans to identify counterfeit products. In this paper, we propose an inspection system using image matching methods to realize the authenticity inspection of brand logos through image recognition. In the first experiment, we compare the similarity evaluation performance by NCC (Normalized Cross-Correlation) and POC (Phase-Only Correlation) using images of actual brand products. In the next experiment, we confirm the effectiveness of logo region detection processing using edge images as preprocessing of image matching with the aim of improving inspection accuracy of images containing many background components. Experimental results show that it is possible to separate genuine and fake more accurately by evaluating similarity by POC. Moreover, we confirmed that by adding the logo region detection processing, the background component of the image was reduced and highly accurate inspection was possible.
Paper Details
Date Published: 22 March 2019
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490R (22 March 2019); doi: 10.1117/12.2521430
Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)
PDF: 6 pages
Proc. SPIE 11049, International Workshop on Advanced Image Technology (IWAIT) 2019, 110490R (22 March 2019); doi: 10.1117/12.2521430
Show Author Affiliations
Satoshi Hirano, Nagoya Institute of Technology (Japan)
Son Lam Phung, Univ. of Wollongong (Australia)
Son Lam Phung, Univ. of Wollongong (Australia)
Published in SPIE Proceedings Vol. 11049:
International Workshop on Advanced Image Technology (IWAIT) 2019
Qian Kemao; Kazuya Hayase; Phooi Yee Lau; Wen-Nung Lie; Yung-Lyul Lee; Sanun Srisuk; Lu Yu, Editor(s)
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