Learning to classify materials using Mueller imaging polarimetry
Résumé
This study investigates the combination of Mueller imaging polarimetry with machine learning for the automated optical classication of raw materials. It shows that standard image classication 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 classication of multispectral depolarization images of real-world materials (banana, wood and foam samples).
Origine : Fichiers produits par l'(les) auteur(s)
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