Abstract
In image-based coin detection, making the image readable is an indispensable part of the feature extraction. However using a 2-D image processing approach for detecting a counterfeit coin is nearly impossible in case of destroyed coins whose textures are severely burnt, sulfated, rusted, or colored.
In this research, we used a 3-D scanner to scan and model an acceptable number of coins capturing height and depth instead of levels of color. The most important advantage of 3-D scanning is to compensate for the above-mentioned destructions of the coin surface. Despite this advantage, we had several unexpected degradations due to shiny coin images. To solve this problem, the 3-D image was decomposed column-wise to a number of separate 1-D signals, which were analyzed separately and restored by the proposed method. This approach gave remarkable results when used to extract valuable features.
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Khazaee, S., Sharifi Rad, M., Suen, C.Y. (2017). Detection of Counterfeit Coins Based on Modeling and Restoration of 3D Images. In: Barneva, R., Brimkov, V., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications. CompIMAGE 2016. Lecture Notes in Computer Science(), vol 10149. Springer, Cham. https://doi.org/10.1007/978-3-319-54609-4_13
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DOI: https://doi.org/10.1007/978-3-319-54609-4_13
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