Surface Defects Detection on Pressure Die Castings by Machine Learning Exploiting Machine Vision Features-Reference-Cited by-同舟云学术

Surface Defects Detection on Pressure Die Castings by Machine Learning Exploiting Machine Vision Features

Published:2022 Issue: Volume: Page:51-61
ISSN:2195-4356
Container-title:Lecture Notes in Mechanical Engineering
language:
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Author:

Papagianni Zoe,Vosniakos George-Christopher^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-06025-0_6

Reference21 articles.

1. Mery, D., Jaeger, T., Dieter, F.: A review of methods for automated recognition of casting defects. Insight-wigst. Northhampton 44(7), 428–436 (2002)

2. Singh, S., Kaur, M.: Machine vision system for automated visual inspection of tile’s surface quality. IOSR J. Eng. 2, 429–432 (2012)

3. Luo, Q., et al.: Automated visual defect classification for flat steel surface: a survey. IEEE Trans. Instrum. Meas. 69, 9329–9349 (2020). https://doi.org/10.1109/TIM.2020.3030167

4. Jia, H., Murphey, Y.L., Shi, J., Chang, T.-S.: An intelligent real-time vision system for surface defect detection. In: Proceedings of the 17th International Conference on Pattern Recognition, pp. 239–242. Cambridge (2004). https://doi.org/10.1109/ICPR.2004.1334512

5. López, F., Valiente, J.M., Prats, J.M., Ferrer, A.: Performance evaluation of soft color texture descriptors for surface grading using experimental design and logistic regression. Pattern Recogn. 41, 1761–1772 (2008). https://doi.org/10.1016/j.patcog.2007.09.011

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