Illumination Environment Research Articles

Shadowplay: Interactions of Artificial Illumination and Darkness

This article explores the aesthetic and philosophical aspects of shadowplay: the multiple and layered interactions between artificial illumination and darkness in urban environments. By examining the concept of shadows through the perspectives of theorists Roy Sorensen and Jun'ichiro Tanizaki, I explore the dynamic tension between darkness, seen as a natural phenomenon, and the fragmented light created by modern technology. The study delves into how artificial light transforms the nocturnal city, influencing both urban life and aesthetic experiences. The first section of the paper addresses darkness in philosophical aesthetics, referencing thinkers such as Kant and Burke, and considers recent research on artificial illumination's ecological and social impacts. The second part analyzes the unique qualities of shadows, drawing from Tanizaki's appreciation for traditional Japanese aesthetics and Sorensen's analytic dissection of shadow phenomena in terms of privation. Through a combination of philosophical inquiry and visual artistic research, the article seeks to provide a deeper understanding of how shadows and artificial light contribute to the aesthetic and emotional experience of contemporary urban nights.

A new target color adaptive graying and segmentation method for gear contact spot detection

Abstract Gear contact spot plays a crucial role in evaluating gear mesh quality. Traditionally, tooth surfaces of gear pairs have been manually brushed with red lead powder and visually inspected after a certain operating period. To enhance detection accuracy and efficiency, computer vision has emerged as an appealing approach for gear contact spot detection. However, determining the graying weights and segmentation threshold poses challenges, particularly in non-ideal illumination environments. To address this problem, this paper proposes a new target color adaptive graying and segmentation (TC-AGS) method for the gear contact spot detection. With the proposed method, the illumination scene is firstly distinguished by the blue color component weight due to its high sensitivity to illumination intensity. Then, an improved adaptively graying algorithm for target color is employed to determine the graying weights for RGB, ensuring the maximum proportion of the desired red color. Finally, either a global threshold method or a local threshold method is selected for image segmentation and contact spot detection according to the illumination intensity. To validate the effectiveness of the proposed method, comprehensive simulation and experimental tests were conducted. The results demonstrate that the proposed method achieves better gear contact spot detection performance than traditional methods. It exhibits higher extraction accuracy under different illumination environments and even in the presence of blurred image sources.

Effects of Sublethal Exposure to Three Water Pollutants on Scototaxis in Rare Minnow (Gobiocypris rarus)

The biological early warning system with fish behavior as the detection index is an efficient and rapid early warning technology for the ecological damage caused by water pollutants. However, the attempt to apply the scototaxis (dark preference) behavior of fish to biological early warning is still relatively lacking. In this study, we delved into the dark and light preferences of the rare minnows (Gobiocypris rarus), employing three distinct tank configurations. Additionally, we systematically examined the modulating effects of environmental illumination, nutritional status, and the number of test subjects on this behavior, aiming to establish optimal experimental parameters for its observation. Furthermore, cadmium ions [Cd2+], tricaine methanesulfonate [MS222], and p-chloroaniline were employed as representative heavy metal ions, neuroactive agents, and organic toxicants, respectively, to test the impact of chemicals on scototaxis in gradient concentrations. The results demonstrated that the rare minnow exhibited a clear scototaxis (dark preference), and this behavior was not affected by the nutritional status of the test fish, the illumination, or the number of subjects. While the dark chamber was consistently the preferred location of rare minnows during the chemical exposure tests, the degree of scototaxis by the rare minnow significantly decreased at Cd2+ ≥ 3 mg/L, MS222 ≥ 11 mg/L, and p-chloroaniline ≥ 29 mg/L, suggesting a potential disruption of their innate behavioral patterns by these chemicals. These findings underscore the sensitivity of rare minnows to water pollutants. Therefore, the scototaxis behavior of rare minnows can be a potential and useful behavioral indicator for biological early warning, which can be used for early biological warning of sudden water pollution caused by chemicals such as Cd2+, MS222, and p-chloroaniline.

Effects of simulated driving task screen background color combinations on visual performance and visual fatigue

ABSTRACT In-car smart devices, such as touchscreen control systems, internet interfaces and hands-free phone functions can cause problems affecting driver comfort, visual fatigue and performance, resulting in decreased reaction times, increasing the likelihood of accidents. This study utilizes a touchscreen display and driving simulator as the operational tools in experiments designed to investigate how changes in screen brightness and different color combinations affect the visual performance and the level of visual fatigue of the driver. Specifically, the aim is to explore the impact of changes in brightness and color combinations on visual fatigue and whether there are interactions between them. During the experimental process, relevant data, including the Critical Fusion Frequency (CFF), touchscreen offset, and the NASA Task Load Index (NASA-TLX), are collected and then analyzed using the SPSS statistical software to provide objective and reliable supporting data and valuable insights to related research fields. The experimental results show that the environmental illumination significantly affects the touchscreen offset, CFF and the NASA-TLX), while the color combination significantly affects touchscreen offset (movement and rotation) and the NASA-TLX.

Deep indoor illumination estimation based on spherical gaussian representation with scene prior knowledge

High dynamic range illumination estimation from a single low dynamic range image is a critical task in the fields of computer vision, graphics and augmented reality. However, directly learning the full HDR environment map or parametric lighting information from a single image is extremely difficult and inaccurate. As a result, we propose a two-stage network approach for illumination estimation that integrates spherical gaussian (SG) representation with scene prior knowledge. In the first stage, a convolutional neural network is utilized to generate material and geometric information about the scene, which serves as prior knowledge for lighting prediction. In the second stage, we model indoor environment illumination using 128 SG functions with fixed center direction and bandwidth, allowing only the amplitude to vary. Subsequently, a Transformer-based lighting parameter regressor is employed to capture the complex relationship between the input images with scene prior information and its SG illumination. Additionally, we introduce a hybrid loss function, which combines a masked loss for high-frequency illumination with a rendering loss for improving the visual quality. By training and evaluating the lighting model on the created SG illumination dataset, the proposed method achieves competitive results in both quantitative metrics and visual quality, outperforming state-of-the-art methods.

Solution-Processed CsPbBr3 Perovskite Photodetectors for Cost-Efficient Underwater Wireless Optical Communication System.

Underwater wireless optical communication (UWOC) has attracted increasing attention due to its advantages in bandwidth, latency, interference resistance, and security. Photodetectors, as a crucial part of receivers, have been continuously developed with the great progress that has been made in advanced materials. Metal halide perovskites emerging as promising optoelectronic materials in the past decade have been used to fabricate various high-performance photodetectors. In this work, high-performance CsPbBr3 perovskite PDs were realized via solution process, with low noise, a high responsivity, and a fast response. Based on these perovskite PDs, a cost-efficient UWOC system was successfully demonstrated on an FPGA platform, achieving a data rate of 6.25 Mbps with a low bit error rate of 0.36%. Due to lower background noise under environment illumination, perovskite PDs exhibit better communication stability before reaching a data rate threshold; however, the BER increases rapidly due to the long fall time, resulting in difficulty in distinguishing switching signals. Reducing the fall time of perovskite PDs and using advanced coding techniques can help to further improve the performance of the UWOC system based on perovskite PDs. This work not only demonstrates the potential of perovskite PDs in the application of UWOC, but also improves the development of a cost-effective UWOC system based on FPGAs.

Assessment of root foraging behaviour in Aralia elata subjected to drought stress under different light spectra

Drought may be more frequent in foreseen decades that will threat non-wood forest products (NWFP) in temperate forests. Plants cope with drought by increasing root foraging ability, which may be also impacted by exposure to light spectra (components of wavelengths in three colours of monochronic lights). In this study, three hybrid light spectra were provided by light-emitting diode (LED) in red (wavelength: 13.6% blue, 60.0% green, 26.4% red), green (2.9% blue, 84.6% green, 2.5% red), and blue (5.4% blue, 77.8% green, 16.9% red) lights for culturing potted Aralia elata seedlings in homogeneous (67.5 mg nitrogen [N] to both halves of pot) and heterogeneous (135 mg N to left half of pot) under drought and well-watered conditions. The red light spectrum was the unique illumination environment where height growth was promoted under well-watered condition and root biomass in fertilized patch was enhanced under drought. Compared to blue light spectrum, red light spectrum increased root foraging scale and precision and placement ratio. Red light spectrum also promoted aboveground biomass, but reduced root collar diameter. No interaction was detected between any pairs of factors among drought, light, and soil nutrient pattern. The heterogeneous pattern increased root to shoot biomass ratio with decreased shoot biomass and increased root foraging sensitivity and precision and fine root placement ratio. Drought enlarged the overall foraging scale with no effects on foraging sensitivity or precision. Overall, exposure to red light spectrum had the potential to promote fine root foraging behaviour under drought, but more trails deserve being tested in the future on a wider range of wavelengths.

Obstacle feature point detection method for real-time processing by monocular camera and line laser

In this study, we propose a method for detecting feature points of obstacles by real-time processing using a monocular camera and a laser. Specifically, we propose an algorithm that detects laser beams emitted on obstacles by binarization, transforms pixel coordinates into distance using triangulation by a laser beam that is obliquely incident on the ground, and estimates the placement angle of the obstacle in real-time processing. No method has been devised to detect the placement angle of obstacles using a monocular camera and a laser. Furthermore, the proposed method does not calculate the linear distance from the camera, but from the position where the camera is moved horizontally parallel to the obstacle in front of the camera, thus detecting the placement angle independently of the placement position of the obstacle. As a result, it was confirmed that the placement angles of obstacles could be detected with a maximum error of 6∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6^{\\circ }$$\\end{document} in six placement positions. Therefore, we succeeded in automatically detecting the placement angle of obstacles in real-time, independent of the illumination of the measurement environment, the reflectance of the obstacle.

Visual Target Interaction Modeling with Multichannel Data Fusion

Facing the demand for accurate, fast, and natural human–computer interaction in the multi-screen control environment, the traditional exchange method of frequent switching control of multi-screen through mouse, keyboard, and other manual methods has problems of low efficiency, weak flexibility, and poor experience. In this paper, we research multi-screen precision control technology based on eye movement to break through the difficulties of precise recognition of human vision under complex environments and poor stability of vision estimation under frequent switching control of multi-screen. This study explores the technology for controlling multiple screens with high precision by tracking eye movements. It overcomes the challenges associated with reliably discerning human visual focus in intricate settings and the instability of visual assessments during rapid transitions between multiple screens. The experimental results of 1143 eye-movement capture tasks are studied, including the accurate recognition technology of human head posture and pupil gaze direction under complex backgrounds, illumination, and different visual field environments, and the theoretical modeling method of multi-screen precise target interaction based on eye-movement is explored. The expression data will be processed using convolutional neural networks, and the dataset will be trained through Python programming and tested on test samples collected by simulated flight crews in the laboratory with an accuracy rate of more than 85%.

Self-supervised reconstruction of re-renderable facial textures from single image

Reconstructing high-fidelity 3D facial texture from a single image is a quite challenging task due to the lack of complete face information and the domain gap between the 3D face and 2D image. Further, obtaining re-renderable 3D faces has become a strongly desired property in many applications, where the term ’re-renderable’ demands the facial texture to be spatially complete and disentangled with environmental illumination. In this paper, we propose a new self-supervised deep learning framework for reconstructing high-quality and re-renderable facial albedos from single-view images in the wild. Our main idea is to first utilize a prior generation module based on the 3DMM proxy model to produce an unwrapped texture and a globally parameterized prior albedo. Then we apply a detail refinement module to synthesize the final texture with both high-frequency details and completeness. To further make facial textures disentangled with illumination, we propose a novel detailed illumination representation that is reconstructed with the detailed albedo together. We also design several novel regularization losses on both the albedo and illumination maps to facilitate the disentanglement of these two factors. Finally, by leveraging a differentiable renderer, each face attribute can be jointly trained in a self-supervised manner without requiring ground-truth facial reflectance. Extensive comparisons and ablation studies on challenging datasets demonstrate that our framework outperforms state-of-the-art approaches.

Mix-VIO: A Visual Inertial Odometry Based on a Hybrid Tracking Strategy.

In this paper, we proposed Mix-VIO, a monocular and binocular visual-inertial odometry, to address the issue where conventional visual front-end tracking often fails under dynamic lighting and image blur conditions. Mix-VIO adopts a hybrid tracking approach, combining traditional handcrafted tracking techniques with Deep Neural Network (DNN)-based feature extraction and matching pipelines. The system employs deep learning methods for rapid feature point detection, while integrating traditional optical flow methods and deep learning-based sparse feature matching methods to enhance front-end tracking performance under rapid camera motion and environmental illumination changes. In the back-end, we utilize sliding window and bundle adjustment (BA) techniques for local map optimization and pose estimation. We conduct extensive experimental validations of the hybrid feature extraction and matching methods, demonstrating the system's capability to maintain optimal tracking results under illumination changes and image blur.

Robust polarimetric dehazing algorithm based on low-rank approximation and multiple virtual-exposure fusion

Polarimetric dehazing is an effective way to enhance the quality of images captured in foggy weather. However, images of essential polarization parameters are vulnerable to noise, and the brightness of dehazed images is usually unstable due to different environmental illuminations. These two weaknesses reveal that current polarimetric dehazing algorithms are not robust enough to deal with different scenarios. This paper proposes a novel, to our knowledge, and robust polarimetric dehazing algorithm to enhance the quality of hazy images, where a low-rank approximation method is used to obtain low-noise polarization parameter images. Besides, in order to improve the brightness stability of the dehazed image and thus keep the image have more details within the standard dynamic range, this study proposes a multiple virtual-exposure fusion (MVEF) scheme to process the dehazed image (usually having a high dynamic range) obtained through polarimetric dehazing. Comparative experiments show that the proposed dehazing algorithm is robust and effective, which can significantly improve overall quality of hazy images captured under different environments.

Adaptive Real-Time Tracking of Molten Metal Using Multi-Scale Features and Weighted Histograms

In this study, we study the tracking of the molten metal region in the dross removal process during metal ingot casting, and propose a real-time tracking method based on adaptive feature selection and weighted histogram. This research is highly significant in metal smelting, as efficient molten metal tracking is crucial for effective dross removal and ensuring the quality of metal ingots. Due to the influence of illumination and temperature in the tracking environment, it is difficult to extract suitable features for tracking molten metal during the metal pouring process using industrial cameras. We transform the images captured by the camera into a multi-scale feature space and select the features with the maximum distinction between the molten metal region and its surrounding background for tracking. Furthermore, we introduce a weighted histogram based on the pixel values of the target region into the mean-shift tracking algorithm to improve tracking accuracy. During the tracking process, the target model updates based on changes in the molten metal region across frames. Experimental tests confirm that this tracking method meets practical requirements, effectively addressing key challenges in molten metal tracking and providing reliable support for the dross removal process.

Spin-Weighted Spherical Harmonics for Polarized Light Transport

The objective of polarization rendering is to simulate the interaction of light with materials exhibiting polarization-dependent behavior. However, integrating polarization into rendering is challenging and increases computational costs significantly. The primary difficulty lies in efficiently modeling and computing the complex reflection phenomena associated with polarized light. Specifically, frequency-domain analysis, essential for efficient environment lighting and storage of complex light interactions, is lacking. To efficiently simulate and reproduce polarized light interactions using frequency-domain techniques, we address the challenge of maintaining continuity in polarized light transport represented by Stokes vectors within angular domains. The conventional spherical harmonics method cannot effectively handle continuity and rotation invariance for Stokes vectors. To overcome this, we develop a new method called polarized spherical harmonics (PSH) based on the spin-weighted spherical harmonics theory. Our method provides a rotation-invariant representation of Stokes vector fields. Furthermore, we introduce frequency domain formulations of polarized rendering equations and spherical convolution based on PSH. We first define spherical convolution on Stokes vector fields in the angular domain, and it also provides efficient computation of polarized light transport, nearly on an entry-wise product in the frequency domain. Our frequency domain formulation, including spherical convolution, led to the development of the first real-time polarization rendering technique under polarized environmental illumination, named precomputed polarized radiance transfer, using our polarized spherical harmonics. Results demonstrate that our method can effectively and accurately simulate and reproduce polarized light interactions in complex reflection phenomena, including polarized environmental illumination and soft shadows.

Cricket: A Self-Powered Chirping Pixel

We present a sensor that can measure light and wirelessly communicate the measurement, without the need for an external power source or a battery. Our sensor, called cricket, harvests energy from incident light. It is asleep for most of the time and transmits a short and strong radio frequency chirp when its harvested energy reaches a specific level. The carrier frequency of each cricket is fixed and reveals its identity, and the duration between consecutive chirps is a measure of the incident light level. We have characterized the radiometric response function, signal-to-noise ratio and dynamic range of cricket. We have experimentally verified that cricket can be miniaturized at the expense of increasing the duration between chirps. We show that a cube with a cricket on each of its sides can be used to estimate the centroid of any complex illumination, which has value in applications such as solar tracking. We also demonstrate the use of crickets for creating untethered sensor arrays that can produce video and control lighting for energy conservation. Finally, we modified cricket's circuit to develop battery-free electronic sunglasses that can instantly adapt to environmental illumination.

ReN Human: Learning Relightable Neural Implicit Surfaces for Animatable Human Rendering

Recently, implicit neural representation has been widely used to learn the appearance of human bodies in the canonical space, which can be further animated using a parametric human model. However, how to decompose the material properties from the implicit representation for relighting has not yet been investigated thoroughly. We propose to address this problem with a novel framework, ReN Human, that takes sparse or even monocular input videos collected in unconstrained lighting to produce a 3D human representation that can be rendered with novel views, poses, and lighting. Our method represents humans as deformable implicit neural representation and decomposes the geometry, material of humans as well as environment illumination for capturing a relightable and animatable human model. Moreover, we introduce a volumetric lighting grid consisting of spherical Gaussian mixtures to learn the spatially varying illumination and animatable visibility probes to model the dynamic self-occlusion caused by human motion. Specifically, we learn the material property fields and illumination using a physically-based rendering layer that uses Monte Carlo importance sampling to facilitate differentiation of the complex rendering integral. We demonstrate that our approach outperforms recent novel views and poses synthesis methods in a challenging benchmark with sparse videos, enabling high-fidelity human relighting.

Exploring impacts of thermal and lighting conditions on office workers’ subjective evaluations, cognitive performance and EEG features in multi-person offices

Modern office workers spend lots of time in multi-person offices. For human-centered building design and operation, it is essential to investigate the influence of coupled physical environments on occupants' well-being and task performance. In this study, to assess impacts of thermal and lighting environments on brainworkers, experiments were conducted under 64 conditions of different temperature, correlated color temperature, and illumination in a multi-person office. Electroencephalogram (EEG) signals were collected to measure the mental state of brainworkers and neurobehavioral tests were utilized to replicate cognitive tasks. The results indicated that only the interaction between the temperature and illumination regarding the sensation of CCT was significant. Primarily, lower illumination in colder environments made subjects perceive the lighting was warmer. Furthermore, cross-domain effects influenced office workers' sensation via synesthetic effects. And the validity of the hue-heat hypothesis on thermal sensation depended on whether subjects received enough stimulation. Besides, for brainworkers, visual comfort peaked at 5000 K and 750 lux but the optimal temperature varied based on specific requirements, with 24 °C promoting overall comfort and 26 °C enhancing executive cognitive performance. The study preliminarily explored the effects among acceptable levels of T, CCT, and I on EEG features, revealing that temperature could adjust occupants’ mental load. However, the further investigation requests equipment with finer temporal granularity, cost-effective techniques and advanced analytical methods to guide office building design and operation.

Illuminator: Image-based illumination editing for indoor scene harmonization

Illumination harmonization is an important but challenging task that aims to achieve illumination compatibility between the foreground and background under different illumination conditions. Most current studies mainly focus on achieving seamless integration between the appearance (illumination or visual style) of the foreground object itself and the background scene or producing the foreground shadow. They rarely considered global illumination consistency (i.e., the illumination and shadow of the foreground object). In our work, we introduce “Illuminator”, an image-based illumination editing technique. This method aims to achieve more realistic global illumination harmonization, ensuring consistent illumination and plausible shadows in complex indoor environments. The Illuminator contains a shadow residual generation branch and an object illumination transfer branch. The shadow residual generation branch introduces a novel attention-aware graph convolutional mechanism to achieve reasonable foreground shadow generation. The object illumination transfer branch primarily transfers background illumination to the foreground region. In addition, we construct a real-world indoor illumination harmonization dataset called RIH, which consists of various foreground objects and background scenes captured under diverse illumination conditions for training and evaluating our Illuminator. Our comprehensive experiments, conducted on the RIH dataset and a collection of real-world everyday life photos, validate the effectiveness of our method.

Achieving Overall Carbon Utilization from Organic Wastewater to Recover Solar Syngas via an Artificial Carbon Cycling System

AbstractBuilding artificial carbon cycling systems for the direct conversion of wastewater into value‐added solar fuels using renewable solar energy can contribute to achieving carbon neutrality. Herein, a bifunctional photocatalyst comprising three‐dimensionally ordered Ti3C2Tx/TiO2 nanoflowers is exploited for tandem carbon cycling systems to achieve efficient organic wastewater treatment with simultaneous CO2 resourcing. The highest efficiency is achieved by using the optimal Ti3C2Tx/TiO2 photocatalyst to treat simulated wastewater containing rhodamine B (RhB), with an enhanced primary CO production rate of 10.02 µmol g−1 h−1 and RhB degradation efficiency of 77.6% after 1 h of illumination in an anoxic environment. The CO/H2 ratio of the produced syngas can be readily tuned from 0.95 to 1.86 by adjusting the Ti3C2Tx content. Mechanistic studies based on DFT, in situ DRIFTS, and LC‐MS2/IC reveal that the coupled photocatalytic RhB degradation and CO2 reduction processes collectively undertake *HCOO intermediate coverage. This phenomenon triggers the sustainable and simultaneous conversion of *CO2 and *HCOO into CO under mild conditions and crosses the key rate‐limiting step of CO2‐to‐CO conversion, thereby achieving overall carbon utilization from organic wastewater treatment. This study offers in‐depth insights into the design and mechanisms of highly effective carbon utilization in artificial carbon cycling systems.

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

Lotus seedpod maturity detection and segmentation in pond environments play a significant role in yield prediction and picking pose estimation for lotus seedpods. However, it is a great challenge to accurately detect and segment lotus seedpods due to insignificant phenotypic differences between the adjacent maturity, changing illumination, overlap, and occlusion of lotus seedpods. The existing research pays attention to lotus seedpod detection while ignoring maturity detection and segmentation problems. Therefore, a semantic segmentation dataset of lotus seedpods was created, where a copy-and-paste data augmentation tool was employed to eliminate the class-imbalanced problem and improve model generalization ability. Afterwards, an improved YOLOv8-seg model was proposed to detect and segment the maturity of lotus seedpods. In the model, the convolutional block attention module (CBAM) was embedded in the neck network to extract distinguished features of different maturity stages with negligible computation cost. Wise-Intersection over Union (WIoU) regression loss function was adopted to refine the regression inference bias and improve the bounding box prediction accuracy. The experimental results showed that the proposed YOLOv8-seg model provides an effective method for “ripe” and “overripe” lotus seedpod detection and instance segmentation, where the mean average precision of segmentation mask (mAPmask) reaches 97.4% and 98.6%, respectively. In addition, the improved YOLOv8-seg exhibits high robustness and adaptability to complex illumination in a challenging environment. Comparative experiments were conducted using the proposed YOLOv8-seg and other state-of-the-art instance segmentation methods. The results showed that the improved model is superior to the Mask R-CNN and YOLACT models, with recall, precision, mAPbox and mAPmask being 96.5%, 94.3%, 97.8%, and 98%, respectively. The average running time and weight size of the proposed model are 25.9 ms and 7.4 M, respectively. The proposed model obtained the highest mAP for lotus seedpod maturity detection and segmentation while maintaining an appropriate model size and speed. Furthermore, based on the obtained segmentation model, 3D visualization of the lotus pond scene is performed, and cloud point of lotus seedpods is generated, which provides a theoretical foundation for robot harvesting in the lotus pond.