Abstract
Hyperspectral imaging captures and analyzes the images from hundreds to thousands of spectral bands, which empowers it to examine the distinctive features that conventional imaging cannot detect. Hyperspectral imaging is a noted technology for the remote sensing domain. But its capacity for ink mismatch detection is still evolving. In this study, we implemented three variants of KNN-fine (F), coarse (CO), and cosine (COS), bagging and boosting ensemble methods, three variants of decision trees-fine (F), Medium (M), and Coarse (CO), and CNN to showcase the impact of varying ink mixing ratios to detect ink mismatch. A comparative analysis with the previous work using the same dataset is illustrated, which indicates that the proposed approach surpasses previous results for blue inks mixed in unequal ratios.
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Jaiswal, G., Sharma, A., Yadav, S.K. (2021). Efficient Ink Mismatch Detection Using Supervised Approach. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_65
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DOI: https://doi.org/10.1007/978-3-030-81462-5_65
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