Evaluating the Utility of Mueller Matrix Imaging for Diffuse Material Classification

Kupinski, Meredith; Li, Lisa

doi:10.2352/j.imagingsci.technol.2020.64.6.060409

Cited by 6 publications

(2 citation statements)

References 24 publications

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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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“…Polarization is an outward manifestation of the transverse wave nature of light and carries information about the structure, material, and reflection angle of the target surface, which can complement the texture information that is passively obtained from the scene. Many polarization imaging techniques have been devised and polarization information is used in a wide range of military and civilian applications, such as in photometrically challenging object attitude estimation [ 1 ], the segmentation identification of objects with special materials (metallic, highly exposed or transparent) [ 2 ], reflection removal [ 3 ], polarization information color constancy [ 4 ], and material classification [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment

Chen,

Hao,

Duan

et al. 2024

Sensors

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Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.

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