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• W3216161393 abstract "Defect detection in the manufacturing industry is of utmost importance for product quality inspection. Recently, optical defect detection has been investigated as an anomaly detection using different deep learning methods. However, the recent works do not explore the use of point pattern features, such as SIFT for anomaly detection using the recently developed set-based methods. In this paper, we present an evaluation of different point pattern feature detectors and descriptors for defect detection application. The evaluation is performed within the random finite set framework. Handcrafted point pattern features, such as SIFT as well as deep features are used in this evaluation. Random finite set-based defect detection is compared with state-of-the-arts anomaly detection methods. The results show that using point pattern features, such as SIFT as data points for random finite set-based anomaly detection achieves the most consistent defect detection accuracy on the MVTec-AD dataset." @default.
• W3216161393 created "2021-12-06" @default.
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• W3216161393 date "2021-01-01" @default.
• W3216161393 modified "2023-10-14" @default.
• W3216161393 title "Point Pattern Feature-Based Anomaly Detection for Manufacturing Defects, in the Random Finite Set Framework" @default.
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• W3216161393 doi "https://doi.org/10.1109/access.2021.3130261" @default.
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