An imaging polarimeter records the polarization state of light reflected by an object that is illuminated with a
polarized source such as a laser. Active polarimetric imagery has been shown to be useful in many remote sensing
applications including shape extraction, material classification and target detection/recognition. In this paper, we
present a method that automatically extracts the angle of incidence, angle of reflection and the relative azimuthal
angle from Mueller matrix imagery. Mueller matrix imagery provides multiple measurements from which we can
construct a nonlinear system of equations. This system is solved using the Levenberg-Marquardt algorithm
which is a standard nonlinear equation solver. We experimentally demonstrate via computer simulations that
the parameter estimates can be estimated accurately using our approach.
A passive imaging polarimeter records the polarization state of light reflected by an object that is illuminated with
an unpolarized and usually uncontrolled source. Passive polarimetric imagery has shown to be useful in many
remote sensing applications including shape extraction, material classification and target detection/recognition.
In this paper, we present an image segmentation algorithm that automatically extracts an object from multi-look
passive polarimetric imagery. The term multi-look refers to multiple polarization measurements where the
position of the source of illumination (typically the Sun in passive systems) changes between measurements. The
proposed method relies on our previous work on estimating the complex index of refraction and reflection angle
from multi-look passive polarimetric imagery. We experimentally showed that the estimates for the index of
refraction were largely invariant to both the position of the source and the view angle. Consequently, we utilize
the index of refraction as a feature vector to design an illumination invariant image segmentation algorithm.
A clustering approach based on the classic c-means algorithm is used for segmenting objects based on their
index of refraction. The proposed segmentation approach is validated by using data collected under laboratory
conditions. Experimental results indicate that the proposed method is effective for segmenting various targets
Passive polarimetric imagery conveys information that complements the information contained in intensity and spectral
imagery. Passive polarimetric measurements have been exploited in many remote sensing applications such as shape
extraction, surface inspection and object detection/recognition. In previous work Thilak et al. proposed an algorithm to
estimate the index of refraction and view angle (object surface orientation) from multiple polarization images where the
source position changes between measurements. That work relies on a specular polarimetric bidirectional reflectance
distribution function (pBRDF) developed by Priest and Meier. The pBRDF incorporates a Mueller matrix that
characterizes the polarized reflection properties of a target for any incident Stokes vector. The results in Thilak et al.
assumed that scattering occurs in the plane of incidence, which means that the pBRDF matrix contains many zero
elements. In this paper, we extend this work to an out-of-plane scattering geometry, which implies that the pBRDF
matrix contains more non-zero elements. In the initial work presented here, a nonlinear optimization approach is utilized
to estimate the incident and reflection angles from a single polarization measurement assuming knowledge of the surface
index of refraction and azimuthal angle between source and receiver. The effectiveness of the proposed method is
verified through computer simulation.
A passive polarization based imaging system records the polarization state of light reflected by objects that are illuminated with an unpolarized and generally uncontrolled source. The information conveyed by the polarization state of light has been exploited in applications such as target detection, shape extraction and material classification. In this paper we present a method to jointly estimate the refractive index and the reflected zenith angle from two measurements collected by a passive polarimeter. An expression for the degree of polarization is derived from the microfacet polarimetric bidirectional reflectance model for the case of scattering in the place of incidence. The parameters of interest are iteratively estimated from polarization measurements assumed to be collected with a polarimeter. Computer simulations are presented to demonstrate the effectiveness of the proposed method.
Passive polarization based imaging is a useful tool in computer vision and pattern recognition. A passive polarization imaging system forms a polarimetric image from the reflection of ambient light that contains useful information for computer vision tasks such as object detection (classification) and recognition. Applications of polarization based pattern recognition include material classification and automatic shape recognition. In this paper, we present two target detection algorithms for images captured by a passive polarimetric imaging system. The proposed detection algorithms are based on Bayesian decision theory. In these approaches, an object can belong to one of any given number classes and classification involves making decisions that minimize the average probability of making incorrect decisions. This minimum is achieved by assigning an object to the class that maximizes the a posteriori probability. Computing a posteriori probabilities requires estimates of class conditional probability density functions (likelihoods) and prior probabilities. A Probabilistic neural network (PNN), which is a nonparametric method that can compute Bayes optimal boundaries, and a -nearest neighbor (KNN) classifier, is used for density estimation and classification. The proposed algorithms are applied to polarimetric image data gathered in the laboratory with a liquid crystal-based system. The experimental results validate the effectiveness of the above algorithms for target detection from polarimetric data.