Snapshot polarization imaging has gained interest in the last few decades. Recent research and technology achievements defined the polarization Filter Array (PFA). It is dedicated to division-of-focal plane polarimeters, which permits to analyze the direction of light electric field oscillation. Its filters form a mosaicked pattern, in which each pixel only senses a fraction of the total polarization states, so the other missing polarization states have to be interpolated. As for Color or Spectral Filter Arrays (CFA or SFA), several dedicated demosaicking methods exist in the PFA literature. Such methods are mainly based on spatial correlation disregarding inter-channel correlation. We show that polarization channels are strongly correlated in images. We therefore propose to extend some demosaicking methods from CFA/SFA to PFA, and compare them with those that are PFA-oriented. Objective and subjective analysis show that the pseudo panchromatic image difference method provides the best results and can be used as benchmark for PFA demosaicking.

Fabric inspection has an importance to prevent the risk of delivering inferior quality product. Until recently, the process was still undertaken offline and manually by humans, which has many drawbacks. The continuous development in computer technology introduces the automated fabric inspection as an effective alternative. In our work, Fast Fourier Transform and Cross-correlation techniques, i.e. linear operations, are first implemented to examine the structure regularity features of the fabric image in the spatial domain. To improve the efficiency of the technique and overcome the problem of detection errors, further thresholding operation is implemented using a level selection filter. Through this filter, the technique is able to detect only the actual or real defects and highlight its exact dimensions. A software package such as Matlab or Scilab is used for this procedure. It is implemented firstly on a simulated plain fabric to determine the most important parameters during the process of defect detection and then to optimize each of them even considering noise. To verify the success of the technique, it is implemented on real plain fabric samples with different colors containing various defects. Several results of the proposed technique for the simulated and real plain fabric structures with the most common defects are presented. Finally, a vision-based fabric inspection prototype that could be accomplished on-loom to inspect the fabric under construction with 100% coverage is proposed.

This paper aims at reviewing the recent published works dealing with industrial applications which rely on polarization imaging. A general introduction presents the basics of polarimetry and then 2D and 3D machine vision application are presented as well as the latest evolution in term of high speed polarimetric imaging.

The electric field that describes electromagnetic radiation can be considered as a vector, whose direction of oscillation is perpendicular to the direction of the wave. This geometrical description of waves is known as polarization.¹ Reptiles or birds, for example, are capable of perceiving polarized information, whereas humans, like any other mammals, are only sensitive to two properties of light: intensity and color. The analysis of the polarization scattered by a scene is known as polarimetric imaging; it yields a complementary performance to classical intensity imaging.² A mathematical tool named Stokes vector is used to describe practically and efficiently the modification of the polarization states when the light travels and interacts with different materials. Polarization analysis using polarimeter instruments becomes increasingly popular in imaging applications, such as classification of materials,³ 3D inspection and reconstruction,⁴ image dehazing,⁵ etc.

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.

In the field of polarimetry, ferroelectric liquid crystal cells are mostly used as bistable polarization rotators suitable to analyze crossed polarizations. This paper shows that, provided such a cell is used at its nominal wavelength and correctly driven, its behavior is close to that of a tunable half-wave plate, and it can be used with much benefit in lightweight imaging polarimetric setups. A partial Stokes polarimeter using a single digital video camera and a single ferroelectric liquid crystal modulator is designed and implemented for linear polarization analysis. Polarization azimuthal angle and degree of linear polarization are available at 150 frames per second with a good accuracy.