Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Quantitative phase imaging, i.e., measuring the map of pathlength shifts due to the specimen of interest, has been developing rapidly over the past decade. The main methods and exciting applications to biomedicine will be reviewed.

Quantitative phase imaging in biomedicine

An objective analysis is carried out of the matricial models representing the polarimetric properties of light and material media leading to the identification and definition of their corresponding physical quantities, using the concept of the coherency matrix. For light, cases of homogeneous and inhomogeneous wavefront are analyzed, and a model for 3D polarimetric purity is constructed. For linear passive material media, a general model is developed on the basis that any physically realizable linear transformation of Stokes vectors is equivalent to an ensemble average of passive, deterministic nondepolarizing transformations. Through this framework, the relevant physical quantities, including indices of polarimetric purity, are identified and decoupled. Some decompositions of the whole system into a set of well-defined components are considered, as well as techniques for isolating the unknown components by means of new procedures for subtracting coherency matrices. These results and methods constitute a powerful tool for analyzing and exploiting experimental and industrial polarimetry. Some particular application examples are indicated.

/pdf/polarimetric-characterization-of-light-and-media-physical-foqb2ja25u.pdf

Polarimetric characterization of light and media - Physical quantities involved in polarimetric phenomena

Preamble.- Preface.- A Brief History and State of the Art of Ellipsometry.-Advanced Mueller Ellipsometry Instrumentation and Data Analysis.- Data Analysis for Nanomaterials: Effective Medium Approximation, its Limits and Implementations.- Relationship between Surface Morphology and Effective Medium Roughness.- Plasmonics and Effective-Medium Theory.- Thin films of Nanostructured Plasmonic Noble Metals.- Spectroscopic Ellipsometry on Metallic Gratings.- Mueller matrix applied to nanostructures.- Spectroscopic Ellipsometry and Magneto-Optical Kerr Spectroscopy of Magnetic Garnet Thin Films Incorporating Plasmonic Nanoparticles.- Generalized Ellipsometry Characterization of Sculptured Thin Films made by Glancing Angle Deposition.- THz Generalized Ellipsometry characterization of highly-ordered 3-dimensional Nanostructures.- Infrared ellipsometric investigations of free carriers and lattice vibrations in superconducting cuprates.- Real-time Ellipsometry for Probing charge-transfer processes at the nanoscale.- Polarimetric and other Optical Probes for the Solid - Liquid Interface.- Spectroscopic Ellipsometry for functional nano-layers of flexible organic electronic devices.- Spectroscopic Ellipsometry of Nanoscale Materials for Semiconductor Device Applications.- Ellipsometry of semiconductor nanocrystals.- Spectroscopic Ellipsometry for Inline Process Control in the Semiconductor Industry.- Thin film applications in research and industry characterized by spectroscopic ellipsometry.- Ellipsometry and Correlation Measurements.- Nanotechnology: Applications and markets, present and future.

/pdf/ellipsometry-at-the-nanoscale-38wvinp9oo.pdf

Ellipsometry at the nanoscale

The design of an optimal Mueller imaging polarimeter is not obvious: it necessitates the use of a well-conditioned polarization state generator as well as a well-conditioned polarization state analyser that permits proper inversion of intensity images to produce the Mueller matrix images. Even though a rigorous calibration procedure is applied, there remain uncertainties on measurements due to the non-ideal optical elements, misalignment, and photon noise in CCD cameras. The relationship between system conditioning and signal-to-noise ratio in an imaging polarimeter has been largely addressed in the recent literature. Herein, a different approach is used to study the error propagation and the impact of noise on the measurements in such a calibrated system quantitatively. The main contributions of this paper are: an extended theory of noise and errors, a development of the effect of noise resulting in a different merit function, a novel polarimeter solution using two retarders, and a practical demonstration of the sensitivity to noise, which are of interest for polarimetric imaging system designers.

https://iopscience.iop.org/article/10.1088/1464-4258/8/9/015/pdf

Optimal configurations for imaging polarimeters: impact of image noise and systematic errors

In the framework of Stokes parameters imaging, polarization-encoded images have four channels which makes physical interpretation of such multidimensional structures hard to grasp at once Furthermore, the information content is intricately combined in the parameters channels which involve the need for a proper tool that allows the analysis and understanding this kind of images In this paper we address the problem of analyzing polarization-encoded images and explore the potential of this information for classification issues and propose ad hoc color displays as an aid to the interpretation of physical properties content The color representation schemes introduced hereafter employ a technique that uses novel Poincare Sphere to color spaces mapping coupled with a segmentation map as an a priori information in order to allow, at best, a distribution of the information in the appropriate color space The segmentation process relies on the fuzzy C-means clustering algorithms family where the used distances were redefined in relation with our images specificities Local histogram equalization is applied to each class in order to bring out the intra-class’s information smooth variations The proposed methods are applied and validated with Stokes images of biological tissues

Physical interpretation of polarization-encoded images by color preview.

Polarization-encoded imaging consists of the distributed measurements of polarization parameters for each pixel of an image. We address clustering of multidimensional polarization-encoded images. The spatial coherence of polarization information is considered. Two methods of analysis are proposed: polarization contrast enhancement and a more-sophisticated image-processing algorithm based on a Markovian model. The proposed algorithms are applied and validated with two different Mueller images acquired by a fully polarimetric imaging system.

http://miv.u-strasbg.fr/collet/ftp/Publis/R14.pdf

Clustering of polarization-encoded images

This paper extends and refines previous work on clustering of polarization-encoded images. The polarization-encoded images used in this work are considered as multidimensional parametric images where a clustering scheme based on Markovian Bayesian inference is applied. Hidden Markov Chains Model (HMCM) and Hidden Hierarchical Markovian Model (HHMM) show to handle effectively Mueller images and give very good results for biological tissues (vegetal leaves). Pretreatments attempting to reduce the image dimensionality based on the Principal Component Analysis (PCA) turns out to be useless for Mueller matrix images.

http://miv.u-strasbg.fr/collet/ftp/Publis/R16.pdf

Clustering of Mueller matrix images for skeletonized structure detection.

In this paper, we introduce a general Bayesian approach to estimate polarization parameters in the Stokes imaging framework. We demonstrate that this new approach yields a neat solution to the polarimetric data reduction problem that preserves the physical admissibility constraints and provides a robust clustering of Stokes images in regard to image noises. The proposed approach is extensively evaluated by using synthetic simulated data and applied to cluster and retrieves the Stokes image issuing from a set of real measurements.

Jihad Zallat

Papers

Optimal configurations for imaging polarimeters: impact of image noise and systematic errors

Physical interpretation of polarization-encoded images by color preview.

Clustering of polarization-encoded images

Clustering of Mueller matrix images for skeletonized structure detection.

Polarimetric data reduction: a Bayesian approach.