Learning to classify materials using Mueller imaging polarimetry

Quéau, Yvain; Leporcq, Florian; Lechervy, Alexis; Alfalou, Ayman

doi:10.1117/12.2516351

Cited by 6 publications

(3 citation statements)

References 14 publications

(11 reference statements)

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“…Mueller matrix estimation can be used to study and classify materials [ 33 , 34 , 35 , 36 , 37 ] or for biomedical applications [ 38 , 39 ]; however, in the following, we will restrict ourselves to Stokes estimation.…”

Section: Embedded Polarization Imagingmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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This review article aims to address common research questions in passive polarized vision for robotics. What kind of polarization sensing can we embed into robots? Can we find our geolocation and true north heading by detecting light scattering from the sky as animals do? How should polarization images be related to the physical properties of reflecting surfaces in the context of scene understanding? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying future directions in passive polarized vision for robotics. After an introduction, three key interconnected areas will be covered in the following sections: embedded polarization imaging; polarized vision for robotics navigation; and polarized vision for scene understanding. We will then discuss how polarized vision, a type of vision commonly used in the animal kingdom, should be implemented in robotics; this type of vision has not yet been exploited in robotics service. Passive polarized vision could be a supplemental perceptive modality of localization techniques to complement and reinforce more conventional ones.

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Section: Embedded Polarization Imagingmentioning

confidence: 99%

Passive Polarized Vision for Autonomous Vehicles: A Review

Serres,

Lapray,

Viollet

et al. 2024

Sensors

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“…The latter could be utilized to mimic human-like intellect when handling large and complex datasets, images, etc. Being part of AI, the vastly expanding field of machine learning (ML) covers a wide spectrum of applications for solving multiple scientific problems [47][48][49][50][51][52][53] as well as for cancer classification [54][55][56][57][58][59][60][61][62]. Since conventional programming processes an input data by means of particular syntax and semantics to produce a desired output, such a method is prone to multiple errors repetition.…”

Section: Introductionmentioning

confidence: 99%

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

et al. 2022

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In biophotonics, novel techniques and approaches are being constantly sought to assist medical doctors and to increase both sensitivity and specificity of the existing diagnostic methods. In such context, tissue polarimetry holds promise to become a valuable optical diagnostic technique as it is sensitive to tissue alterations caused by different benign and malignant formations. In our studies, multiple Mueller matrices were recorded for formalin-fixed, human, ex vivo colon specimens containing healthy and tumor zones. The available data were pre-processed to filter noise and experimental errors, and then all Mueller matrices were decomposed to derive polarimetric quantities sensitive to malignant formations in tissues. In addition, the Poincaré sphere representation of the experimental results was implemented. We also used the canonical and natural indices of polarimetric purity depolarization spaces for plotting our experimental data. A feature selection was used to perform a statistical analysis and normalization procedure on the available data, in order to create a polarimetric model for colon cancer assessment with strong predictors. Both unsupervised (principal component analysis) and supervised (logistic regression, random forest, and support vector machines) machine learning algorithms were used to extract particular features from the model and for classification purposes. The results from logistic regression allowed to evaluate the best polarimetric quantities for tumor detection, while the use of random forest yielded the highest accuracy values. Attention was paid to the correlation between the predictors in the model as well as both losses and relative risk of misclassification. Apart from the mathematical interpretation of the polarimetric quantities, the presented polarimetric model was able to support the physical interpretation of the results from previous studies and relate the latter to the samples’ health condition, respectively.

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“…In remote sensing, classification of ground targets from radar polarimetry combined with statistical decision-making techniques are well established [14]. Material classification for visible and near visible Mueller matrix imaging has been tested with a small field of view bench top imaging systems with an emphasis on post-processing algorithms and polarized light scattering models [15][16][17][18]. As the diffuse reflectance of a material increases, the degree of polarization of the scattered light decreases; this is known as the Umov Effect [19].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Utility of Mueller Matrix Imaging for Diffuse Material Classification

Kupinski¹,

Li²

2020

jist

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Evaluating the utility of polarimetric imaging for material identification, as compared to conventional irradiance imaging, motivates this work. Images of diffuse objects captured with a wide field of view Mueller matrix polarimeter are used to demonstrate a classification and measurement optimization method. This imaging study is designed to test polarimetric utility in discriminating white fabric from white wood. The material color is constrained to be similar so that classification from only total radiance imaging is difficult, i.e., metamerism. A statistical divergence between two distributions of measured intensity is used to optimize the Polarization State Generator (PSG) and the Polarization State Analyzer (PSA) given two classes of Mueller matrices. The classification performance as a function of number of polarimetric measurements is computed. This work demonstrates that two polarimetric measurements of white fabric and white wood offer nearly perfect classification. The utility and design of partial Mueller imaging is supported by this optimization of PSG/PSA states and number of measurements.

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Learning to classify materials using Mueller imaging polarimetry

Cited by 6 publications

References 14 publications

Passive Polarized Vision for Autonomous Vehicles: A Review

Passive Polarized Vision for Autonomous Vehicles: A Review

Polarization-Based Histopathology Classification of Ex Vivo Colon Samples Supported by Machine Learning

Evaluating the Utility of Mueller Matrix Imaging for Diffuse Material Classification

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