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 feasibility of such an approach is empirically demonstrated through the classication of multispectral depolarization images of real-world materials (banana, wood and foam samples).
This study is concerned with the design of a Mueller imaging polarimeter for the visualization of spatiallyvarying Mueller matrix fields. A simplified calibration procedure is advocated, where all the optical elements are calibrated simultaneously rather than independently as in the state-of-the-art. This is shown to significantly reduce the bias inherent to sequential calibration methods. In addition, this procedure requires no reference sample, it allows calibration both in transmission or in reflection modes, and it relies on readyto-use cameras. Put together, these novelties should help non-specialists in optics designing and calibrating a Mueller imaging polarimeter for applications such as material classification.
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