Camera characterization for improving color archaeological documentation

[EN] Determining the correct color is essential for proper cultural heritage documentation and cataloging. However, the methodology used in most cases limits the results since it is based either on perceptual procedures or on the application of color profiles in digital processing software. The objective of this study is to establish a rigorous procedure, from the colorimetric point of view, for the characterization of cameras, following different polynomial models. Once the camera is characterized, users obtain output images in the sRGB space that is independent of the sensor of the camera. In this article we report on pyColorimetry software that was developed and tested taking into account the recommendations of the Commission Internationale de l’Eclairage (CIE). This software allows users to control the entire digital image processing and the colorimetric data workflow, including the rigorous processing of raw data. We applied the methodology on a picture targeting Levantine rock art motifs in Remigia Cave (Spain) that is considered part of a UNESCO World Heritage Site. Three polynomial models were tested for the transformation between color spaces. The outcomes obtained were satisfactory and promising, especially with RAW files. The best results were obtained with a second-order polynomial model, achieving residuals below three CIELAB units. We highlight several factors that must be taken into account, such as the geometry of the shot and the light conditions, which are determining factors for the correct characterization of a digital camera.The authors gratefully acknowledge the support from the Spanish Ministerio de Economia y Competitividad to the project HAR2014-59873-R. The authors would like also to acknowledge the comments from the colleagues at the Photogrammetry & Laser Scanning Research Group (GIFLE) and the fruitful discussions provided by Archaeologist Dr. Esther Lopez-Montalvo.Molada Tebar, A.; Lerma García, JL.; Marqués Mateu, Á. (2017). Camera characterization for improving color archaeological documentation. Color Research and Application. 43(1):47-57. https://doi.org/10.1002/col.22152S475743

The development of local solar irradiance for outdoor computer graphics rendering

Atmospheric effects are approximated by solving the light transfer equation, LTE, of a given viewing path. The resulting accumulated spectral energy (its visible band) arriving at the observer’s eyes, defines the colour of the object currently on the line of sight. Due to the convenience of using a single rendering equation to solve the LTE for daylight sky and distant objects (aerial perspective), recent methods had opt for a similar kind of approach. Alas, the burden that the real-time calculation brings to the foil had forced these methods to make simplifications that were not in line with the actual world observation. Consequently, the results of these methods are laden with visual-errors. The two most common simplifications made were: i) assuming the atmosphere as a full-scattering medium only and ii) assuming a single density atmosphere profile. This research explored the possibility of replacing the real-time calculation involved in solving the LTE with an analytical-based approach. Hence, the two simplifications made by the previous real-time methods can be avoided. The model was implemented on top of a flight simulator prototype system since the requirements of such system match the objectives of this study. Results were verified against the actual images of the daylight skies. Comparison was also made with the previous methods’ results to showcase the proposed model strengths and advantages over its peers

Flower colours through the lens: Quantitative measurement with visible and ultraviolet digital photography

Background: The study of the signal-receiver relationship between flowering plants and pollinators requires a capacity to accurately map both the spectral and spatial components of a signal in relation to the perceptual abilities of potential pollinators. Spectrophotometers can typically recover high resolution spectral data, but the spatial component is difficult to record simultaneously. A technique allowing for an accurate measurement of the spatial component in addition to the spectral factor of the signal is highly desirable

Imaging multi-age construction settlement behaviour by advanced SAR interferometry

This paper focuses on the application of Advanced Satellite Synthetic Aperture Radar Interferometry (A-DInSAR) to subsidence-related issues, with particular reference to ground settlements due to external loads. Beyond the stratigraphic setting and the geotechnical properties of the subsoil, other relevant boundary conditions strongly influence the reliability of remotely sensed data for quantitative analyses and risk mitigation purposes. Because most of the Persistent Scatterer Interferometry (PSI) measurement points (Persistent Scatterers, PSs) lie on structures and infrastructures, the foundation type and the age of a construction are key factors for a proper interpretation of the time series of ground displacements. To exemplify a methodological approach to evaluate these issues, this paper refers to an analysis carried out in the coastal/deltaic plain west of Rome (Rome and Fiumicino municipalities) affected by subsidence and related damages to structures. This region is characterized by a complex geological setting (alternation of recent deposits with low and high compressibilities) and has been subjected to different urbanisation phases starting in the late 1800s, with a strong acceleration in the last few decades. The results of A-DInSAR analyses conducted from 1992 to 2015 have been interpreted in light of high-resolution geological/geotechnical models, the age of the construction, and the types of foundations of the buildings on which the PSs are located. Collection, interpretation, and processing of geo-thematic data were fundamental to obtain high-resolution models; change detection analyses of the land cover allowed us to classify structures/infrastructures in terms of the construction period. Additional information was collected to define the types of foundations, i.e., shallow versus deep foundations. As a result, we found that only by filtering and partitioning the A-DInSAR datasets on the basis of the above-mentioned boundary conditions can the related time series be considered a proxy of the consolidation process governing the subsidence related to external loads as confirmed by a comparison with results from a physically based back analysis based on Terzaghi's theory. Therefore, if properly managed, the A-DInSAR data represents a powerful tool for capturing the evolutionary stage of the process for a single building and has potential for forecasting the behaviour of the terrain-foundation-structure combination

Image based surface reflectance remapping for consistent and tool independent material appearence

Physically-based rendering in Computer Graphics requires the knowledge of material properties other than 3D shapes, textures and colors, in order to solve the rendering equation. A number of material models have been developed, since no model is currently able to reproduce the full range of available materials. Although only few material models have been widely adopted in current rendering systems, the lack of standardisation causes several issues in the 3D modelling workflow, leading to a heavy tool dependency of material appearance. In industry, final decisions about products are often based on a virtual prototype, a crucial step for the production pipeline, usually developed by a collaborations among several departments, which exchange data. Unfortunately, exchanged data often tends to differ from the original, when imported into a different application. As a result, delivering consistent visual results requires time, labour and computational cost. This thesis begins with an examination of the current state of the art in material appearance representation and capture, in order to identify a suitable strategy to tackle material appearance consistency. Automatic solutions to this problem are suggested in this work, accounting for the constraints of real-world scenarios, where the only available information is a reference rendering and the renderer used to obtain it, with no access to the implementation of the shaders. In particular, two image-based frameworks are proposed, working under these constraints. The first one, validated by means of perceptual studies, is aimed to the remapping of BRDF parameters and useful when the parameters used for the reference rendering are available. The second one provides consistent material appearance across different renderers, even when the parameters used for the reference are unknown. It allows the selection of an arbitrary reference rendering tool, and manipulates the output of other renderers in order to be consistent with the reference

The Digitisation of the Sputter Deposition Process of Transparent Conductive Oxides by Implementing Artificial Intelligence

Plasma-based sputtering is extensively employed to fabricating thin film Transparent Conductive Oxides (TCOs), a category of semiconducting material used for a wide variety application from flat panel display to energy harvesting devices. Methods of evaluating the plasma i.e. glow discharge has been greatly studied requiring complex theoretical physics which is not viable for applied/materials scientist who frequently use this method of deposition at an operational level. The aim of the project was to explore new methods of characterizing the plasma sputtering process to evaluate the possibility of simplifying the monitoring and assessment of the sputtering process. The first method involved monitoring the RF-based plasma sputtering process through optical spectroscopy and characterizing the discharge based on its specific colour. The 2nd method involved implementing Artificial intelligence/Machine learning and feeding the emission spectrum of the plasma extracted from an array of depositions to a deep learning model to evaluate the accuracy of predicting not only the properties of the deposited TCO film but also the growth process conditions. Implementation of such methods pave the way for the design of a digital shadow for plasma-based deposition in the material engineering industry. Spectral data from the plasma was obtained by placing an in-vacuum collimator optic probe (Plasus) which featured a unique honeycomb structure capturing photons whilst simultaneously trapping sputtering particles and preventing gradual coating of the collimator’s quartz window. The spectrometer was programmed to calculate the area under the peak of the spectral range based on predesignated segments of the spectrum. In addition to this, the light collected from the plasma was also guided to a 2nd spectrometer (Jeti) that calculates the chromaticity index of the light. The colour of the plasma source was deduced based on conventional chromaticity index analysis and it was compared to the direct spectral data plots of the emission peaks to investigate the possibility of characterizing the plasma based on its specific colour. This technique was demonstrated to be a viable potential for evaluating the plasma in terms of providing information regarding the stability of the plasma, chamber pressure and plasma power. A linear relationship between the colour functions and the plasma power was observed, while the stability of the sputtering plasma can be assessed based on the plasma colour functions. The colour functions also follow a unique pattern when the working gas pressure is increased. The spectral properties and colour functions of a radio frequency (RF)-based sputtering plasma source was also monitored during consecutive sputter deposition of Indium doped zinc oxide (IZO) thin films under argon and argon/hydrogen mix. The effect of target exposure to the hydrogen gas on charge density/mobility and spectral transmittance of the deposited films was investigated. Consecutive exposure to the hydrogen gas during the deposition process progressively affects the properties of thin films with a certain degree of continuous improvement in electrical conductivity while demonstrating that reverting to only argon from argon/hydrogen mix follows a complex pathway. Preparation of highly conductive zinc oxide thin films without indium presence was exhibited eliminating the need for the expensive indium addition. The complexity of the reactive sputtering of highly conductive zinc oxide thin films in the presence of hydrogen at room temperature was investigated. A hypothesis was put forward regarding importance ii of precise geometric positioning of the substrate with respect to the magnetron to achieve maximum conductivity. The electrical properties of the deposited zinc oxide thins films based on their position on the substrate holder relative to the magnetron were examined. Machine Learning/Deep learning models were incorporated to examine the accuracy of predicting a single feature (sheet resistance) of thin films of indium-doped zinc oxide deposited via plasma sputter deposition by feeding the spectral data of the plasma to the deep learning models. It was shown that Artificial Neural networks could be implemented as a model that could predict the sheet resistance of the thin films as they were deposited, taking in only the spectral emission of the plasma as an input. The spectral emission data from the plasma glow of various sputtering targets containing indium oxide, zinc oxide, and tin oxide were obtained. These spectral data were then converted into twodimensional arrays by implementing a basic array-reshaping technique and a more complex procedure utilizing an unsupervised deep-learning technique, known as the self-organizing-maps method. The twodimensional images obtained from each single-emission spectrum of the plasma mimic an image that can then be used to train a convolutional neural network model capable of predicting certain plasma features, such as impurity levels in the sputtering target, working gas composition, plasma power, and chamber pressure during the machine operation. It was demonstrated that that the single-array-to-2D-array conversion technique, coupled with deep-learning techniques and computer vision, can achieve high predictive accuracy and can, therefore, be fundamental to the construction of a sputtering system’s digital twin

Application of Radial Distribution Functions to Diffraction and Imaging Data: Interfacial Structures, Amorphous, Disordered Materials

The central theme of this thesis is the application of radial and pair distribution function analysis to materials characterisation problems for nanotechnology. These concepts are introduced in Chapter 1, and the associated methods are described in Chapter 2. Chapter 3 details the first of the results which discusses the design and development of a software tool called ImageDataExtractor. This auto-extracts microscopy images and then analyses them to afford quantitative information regarding particles in a sample, such as shape, size and distribution. It realises an opportunity for data-mining the ubiquity of readily available images in the literature. Chapter 4 presents results of the development and execution of a novel experimental technique, called glancing-angle pair distribution function (gaPDF) analysis, applied to the structure of the working electrode in dye-sensitised solar cells (DSSCs). This structure was successfully observed, validating this novel method. The investigation also suggested preferred binding modes of the carboxylic acid anchoring groups present in this interfacial structure. Chapters 5 and 6 demonstrate the application of PDF analysis to synchrotron-based powder diffraction data of two material case studies: the rare earth phosphate glass (REPG) (Gd2O3)0.230(P2O5)0.770, and four Ru based photo-isomers. The closest R…R rare earth separation, which governs optical properties of REPGs, was determined to be 4.2(1) Å, aided by various statistical techniques. Analysis on four Ru-based photo-isomers confirmed: the existence of local structure in such compounds, their ability to be photo-isomerised in powder form, the theoretical models constructed using computational techniques, and the lack of heterogeneity in photo-isomerisation throughout a given light-induced sample. Chapter 7 concludes the work and offers a future outlook

Computational statistics using the Bayesian Inference Engine

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasises hybrid tempered MCMC schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE is implements a full persistence or serialisation system that stores the full byte-level image of the running inference and previously characterised posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GPL.Comment: Resubmitted version. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GP

QUIJOTE Scientific Results. II. Polarisation Measurements of the Microwave Emission in the Galactic molecular complexes W43 and W47 and supernova remnant W44

We present Q-U-I JOint TEnerife (QUIJOTE) intensity and polarisation maps at 10-20 GHz covering a region along the Galactic plane 24<l<45 deg, |b|<8 deg. These maps result from 210 h of data, have a sensitivity in polarisation of ~40 muK/beam and an angular resolution of ~1 deg. Our intensity data are crucial to confirm the presence of anomalous microwave emission (AME) towards the two molecular complexes W43 (22 sigma) and W47 (8 sigma). We also detect at high significance (6 sigma) AME associated with W44, the first clear detection of this emission towards a SNR. The new QUIJOTE polarisation data, in combination with WMAP, are essential to: i) Determine the spectral index of the synchrotron emission in W44, beta_sync =-0.62 +/-0.03, in good agreement with the value inferred from the intensity spectrum once a free-free component is included in the fit. ii) Trace the change in the polarisation angle associated with Faraday rotation in the direction of W44 with rotation measure -404 +/- 49 rad/m2. And iii) set upper limits on the polarisation of W43 of Pi_AME <0.39 per cent (95 per cent C.L.) from QUIJOTE 17~GHz, and <0.22 per cent from WMAP 41 GHz data, which are the most stringent constraints ever obtained on the polarisation fraction of the AME. For typical physical conditions (grain temperature and magnetic field strengths), and in the case of perfect alignment between the grains and the magnetic field, the models of electric or magnetic dipole emissions predict higher polarisation fractions.Comment: Accepted for publication in MNRA