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On Combining Convolutional Autoencoders and Support Vector Machines for Fault Detection in Industrial Textures
Defects in textured materials present a great variability, usually requiring ad-hoc solutions for each specific case. This research work proposes a solution that combines two machine learning-based approaches, convolutional autoencoders, CA; one class support vector machines, SVM. Both methods are t...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2021
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150843/ https://www.ncbi.nlm.nih.gov/pubmed/34064975 http://dx.doi.org/10.3390/s21103339 |