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Proceedings Paper

Learning to classify materials using Mueller imaging polarimetry
Author(s): Yvain Quéau; Florian Leporcq; Alexis Lechervy; Ayman Alfalou
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Paper 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).

Paper Details

Date Published: 16 July 2019
PDF: 7 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720Z (16 July 2019); doi: 10.1117/12.2516351
Show Author Affiliations
Yvain Quéau, GREYC, UMR, CNRS, Univ. de Caen Normandie (France)
Florian Leporcq, INP-ENSEEIHT, Univ. Fédérale Toulouse-Midi-Pyrénées (France)
Alexis Lechervy, GREYC, UMR, CNRS, Univ. de Caen Normandie (France)
Ayman Alfalou, Institut Supérieur d'Electronique du Nord (France)


Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)

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