AN OVERVIEW OF THE IMPACT OF STRAY LIGHT FROM COMMERCIAL GREENHOUSES

Stray light from commercial greenhouses is becoming a significant problem causing disruption to wildlife activity and annoyance for local residents. To quantify the amount of stray light from a typical large greenhouse the authors have modelled several lighting installations based on a range of generic horticultural light sources. The impact of the stray light is dependent on the spectral power distribution of the sources employed, the intensity and distribution. Current standards for obtrusive light from outdoor work places do not seem to be suitable when applied to greenhouses.

DAMAGE REDUCTION WITH MAINTAINED COLOUR QUALITY OF ARTWORK UNDER RGB PROJECTOR

Light absorbed by sensitive artwork may cause irreversible damage. Optimising the spectral power distribution of light sources to minimise absorbed light can reduce damage while maintaining the colour appearance of artwork. Previous absorption minimisation studies used optimisation methods without comparing their performance. Here, three channel RGB LED projector spectra was optimised for 24 colour samples by using brute-force (BF) and a multi-objective genetic algorithm (MOGA). BF search and MOGA were similar in achieving optimal results, reducing both light absorption and energy consumption by almost half. MOGA was 2.5 times faster than BF in finding optimal solutions. The results indicate that an LED RGB projector can be used to illuminate museum artifacts to reduce light absorption and energy consumption, with the caveat of perceptible colour shifts in certain samples. Future research will investigate the use of CIECAM02 instead of CIEDE2000 and observers’ subjective evaluations of artwork under optimised lighting.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts’ involvement. As also revealed by our study, experts’ opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts’ knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community.

Reflectance estimation from snapshot multispectral images captured under unknown illumination

Multispectral images contain more spectral information of the scene objects compared to color images. The captured information of the scene reflectance is affected by several capture conditions, of which the scene illuminant is dominant. In this work, we implemented an imaging pipeline for a spectral filter array camera, where the focus is the estimation of the scene reflectances when the scene illuminant is unknown. We simulate three scenarios for reflectance estimation from multispectral images, and we evaluate the estimation accuracy on real captured data. We evaluate two camera model-based reflectance estimation methods that use a Wiener filter, and two other linear regression models for reflectance estimation that do not require an image formation model of the camera. Regarding the model-based approaches, we propose to use an estimate for the illuminant's spectral power distribution. The results show that our proposed approach stabilizes and marginally improves the estimation accuracy over the method that estimates the illuminant in the sensor space only. The results also provide a comparison of reflectance estimation using common approaches that are suited for different realistic scenarios.

Methods to improve colour mismatch between displays

With the rapid development of display technology, the colour mismatch of the colours having same tristimulus values between devices is an urgent problem to be solved. This is related to the wellknown problem of observer metamerism, caused by the spectral power distribution (SPD) of primaries and the difference between individual observers' and the standard CIE colour matching functions. An experiment was carried out for 20 observers to perform colour matching of colour stimuli with a field-of-view of 4° between 5 displays, including two LCD and two OLED, against a reference LCD display. The results were used to derive a matrixbased colour correction method. The method was derived from colorimetric visually matched colorimetric data. Furthermore, different colour matching functions were evaluated to predict the degree of observer metamerism. The results showed that the correction method gave satisfactory results. Finally, it was found that the use of 2006 2° colour matching function outperformed 1931 2° CMFs with a large margin, most marked between an OLED and an LCD display.

Correlated color temperature: Use and limitations

Correlated color temperature (CCT) is a one-dimensional metric that aims to quantify the perceived visual quality of nominal white light sources. It is often used as a proxy for the color quality of light sources due to its ease of use. However, CCT lacks the accuracy in communicating color information for research purposes. Two light sources with identical CCTs can appear perceptually different, and these differences are not estimated by CCT due to the loss of information caused by reducing spectral power distribution of light sources into a one-dimensional metric. Using supplemental metrics in addition to CCT, providing the absolute spectral power distribution of light sources in graphical and tabular form, and documenting and accounting for potential confounding factors, such as chromatic adaptation, can increase the validity of research results, improve the repeatability of studies, and help address replication concerns.