Learning to classify materials using Mueller imaging polarimetry

Yvain Quéau; Florian Leporcq; Alexis Lechervy; Ayman Alfalou

doi:10.1117/12.2516351

16 July 2019 Learning to classify materials using Mueller imaging polarimetry

Yvain Quéau, Florian Leporcq, Alexis Lechervy, Ayman Alfalou

Proceedings Volume 11172, Fourteenth International Conference on Quality Control by Artificial Vision; 111720Z (2019) https://doi.org/10.1117/12.2516351
Event: Fourteenth International Conference on Quality Control by Artificial Vision, 2019, Mulhouse, France

Abstract

This study investigates the combination of Mueller imaging polarimetry with machine learning for the automated optical classification of raw materials. It shows that standard image classification techniques based on support vector machines or deep neural networks can readily be applied to polarimetric data extracted from Mueller matrix measurements. The feasability of such an approach is empirically demonstrated through the classification of multispectral depolarization images of real-world materials (banana, wood and foam samples).

Citation Download Citation

Yvain Quéau, Florian Leporcq, Alexis Lechervy, and Ayman Alfalou "Learning to classify materials using Mueller imaging polarimetry", Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720Z (16 July 2019); https://doi.org/10.1117/12.2516351

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