Principal component-based material recognition using laser speckle on an embedded platform

Parama Pal; Rishikesh Kulkarni; Aishwarya SN

doi:10.1117/12.2582709

5 March 2021 Principal component-based material recognition using laser speckle on an embedded platform

Parama Pal, Rishikesh Kulkarni, Aishwarya SN

Proceedings Volume 11704, Vertical-Cavity Surface-Emitting Lasers XXV; 117040N (2021) https://doi.org/10.1117/12.2582709
Event: SPIE OPTO, 2021, Online Only

Abstract

Optical speckle patterns relate to the topological characteristics of the scattering surface through commonly used parameters such as contrast and various correlation functions. We use a probe with a single-mode laser source, lenses, and associated electronics for fast acquisition of large sets of images of distinct material types. We use the principal component analysis (PCA) technique for generating dictionaries of images for speckle image datasets of known materials. We subsequently acquire speckle data for an unknown material and represent it via its orthogonal basis vectors and use least square errors for accurate classification of the unknown material.

Conference Presentation

Citation Download Citation

Parama Pal, Rishikesh Kulkarni, and Aishwarya SN "Principal component-based material recognition using laser speckle on an embedded platform", Proc. SPIE 11704, Vertical-Cavity Surface-Emitting Lasers XXV, 117040N (5 March 2021); https://doi.org/10.1117/12.2582709

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