Color Modeling Research Articles

Material and Technical Analysis as a Support for Art-Historical Characterization of Selected Mural Paintings in Austria around 1400

Several medieval mural cycles in Austria were studied from the material and technical point of view, aiming to confirm (or reject) the art-historical hypothesis of their stylistic and workshop connection. These paintings can be found in the churches of Rust (“Fischerkirche”), Marz (Virgin’s Coronation parish church), Kobenz (St. Ruprecht parish church), Ofenbach (St. Veid parish church), and St. Johann am Steinfelde (St. John parish church). They were carried out around 1400 in the International Gothic style. Their workshop connections based on the style are doubtful, therefore, a material and technical study was carried out. Results showed different plaster composition, similar pigment palette, and diverse painting procedures. The murals in Marz, St. Johann, and the older register in Rust reveal important similarities such as plaster composition, predominant a fresco painting technique, the use of natural inorganic pigments, as well as many aspects of the painting procedure and modeling. On the contrary, those in Kobenz, Ofenbach, and the younger register in Rust differ considerably. The second group reveals lower quality in plaster composition, larger a secco parts, addition of synthetic pigments (Kobenz), and a rougher color modeling, indicating less skilled artists. The lower quality also results in a worse conservation state of these murals. The obtained results confirm the same workshop, but different artists in the first group, while in the second group no clear workshop/artist connection could be established.

Comprehensive Metabolomic and Transcriptomic Analysis of the Regulatory Network of Volatile Terpenoid Formation during the Growth and Development of Pears (Pyrus spp. ‘Panguxiang’)

Volatiles are essential substances that determine distinct fruit flavors and user preferences. However, the metabolic dynamic and molecular modulation models that regulate the overall flavor generation during fruit growth and ripening are still largely unclear for most fruit species. To comprehensively analyze the molecular mechanism and regulation mechanism of aroma accumulation and aroma component formation in Pyrus spp. ‘Panguxiang’ (‘Panguxiang’pear), this study compared pear phenotype, sugars, organic acid content, and the expression of related genes and metabolites amid pear growth and development in Pyrus spp. ‘Panguxiang’. A total of 417 VOCs (4 amines, 19 aromatics, 29 aldehydes, 31 alcohols, 38 ketones, 64 heterocyclic compounds, 89 terpenoids, 94 esters, and 49 others) were found. The potential gene expression patterns were explored by combining transcriptomics and metabolomics, and VOC-associated metabolism and transcriptome data from all samples were integrated during the growth and development period. On this basis, we constructed a colorful model depicting changes in the VOCs and genes throughout pear growth and development. Our findings reveal that terpenoid biosynthesis pathways are the main aroma production pathways during pear growth and development. In addition to providing novel insights into the metabolic control of fruit flavor during growth and development, this study also provides a new theoretical basis for studying aroma metabolites in pears.

MUGAN: Thermal Infrared Image Colorization Using Mixed-Skipping UNet and Generative Adversarial Network

Common cameras cannot capture high quality image in the night or extreme weather conditions that without enough light, while thermal infrared(TIR) cameras are not limited in this situation. Hence, TIR imaging technique is widely used in military, surveillance, nighttime traffic and other scenarios. However, TIR images are monochromatic, and the majority of details of such images are lost, which are difficult for human or computer system to analyze. Translating TIR images into visible images is beneficial to subsequent observation or further processing. Though there are some advances to realize the transformation from TIR images to color visible images, edge distortion and semantic confusion remain to be solved. Therefore, we propose a Mixed-Skipping UNet(MS-UNet) based image colorization model joint Generative Adversarial Network, which is denoted by MUGAN. Firstly, the dense skip connections of UNet++ and full-scale skip connections of UNet 3+ are combined to form the MS-UNet, which is regarded as the generator. In addition, we design a feature extraction module in MS-UNet to effectively capture the multi-scale features in source image. Then, a novel attention mechanism module is designed for decoding stage which can help decoder of MS-UNet to focus on the important information. Moreover, we explore the effect of different loss functions in TIR image colorization task, and the loss function with excellent performance is selected to further optimize the training process of the model. Extensive experiments prove the superiority of our method in the task of TIR image colorization. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/HangyingLiao/MUGAN</uri> .

Multiscale models of plasmonic structural colors with nanoscale surface roughness.

Plasmonic coloration arises from resonant interaction between visible light and metallic nanostructures, which causes wavelength-selective absorption or scattering of light. This effect is sensitive to surface roughness that can perturb these resonant interactions and cause observed coloration to deviate from coloration predicted by simulations. We present a computational visualization approach that incorporates electrodynamic simulations and physically based rendering (PBR) to investigate the effect of nanoscale roughness on the structural coloration from thin, planar silver films decorated with nanohole arrays. Nanoscale roughness is modeled mathematically by a surface correlation function and parameterized in terms of roughness that is either out of or into the plane of the film. Our results provide photorealistic visualization of the influence of nanoscale roughness on the coloration from silver nanohole arrays in both reflectance and transmittance. Out-of-plane roughness has a significantly greater effect on coloration than in-plane roughness. The methodology introduced in this work is useful for modeling artificial coloration phenomena.

Anchor-free instance segmentation and fast performance evaluation of Anammox granular sludge

<p indent="0mm">The method of measuring the biochemical reaction equivalence of Anammox granular sludge is accurate but time-consuming, and the method based on image color modeling is fast but inaccurate enough. Therefore, the study proposes a new model combining the visual features of granular sludge (such as color, size, and roughness) for performance evaluation based on the instance segmentation task, which can guarantee high accuracy while performing a fast evaluation. A sampling scheme is first designed to collect the granular sludge images, and then image annotation is performed to obtain the granular sludge image dataset. This study proposes an improved BlendMask anchor-free instance segmentation method, which combines deformable convolution on the backbone network to extract small target information, adopts an adaptive sample selection strategy to balance samples on the head network, and redesigns the modules of postprocessing, loss function, and data augmentation to quickly and accurately extract the granular sludge individuals in image scenes. Then, the association model of visual features and performance is designed to obtain the visual features of granular sludge and discriminate the performance class of the input image. The proposed method can obtain a faster and more accurate evaluation of the performance of granular sludge. Finally, the effectiveness of the proposed method is verified. Compared with the previous methods, the improved BlendMask and the performance evaluation model have improved the accuracy of granular sludge image segmentation and performance evaluation by 3.93% and 1.49%, respectively, achieving the most advanced performance.

Weak-Strong Self-Adapting Fuzzy Neural Classifier for Dynamic Object Detection in RGBD Videos

This paper presents a fuzzy neural method to model background from videos in order to detect dynamic objects. The method includes a weak fuzzy classifier that performs an initial foreground and background separation based on color and depth differences between the actual frame and background models. The outputs of this fuzzy system are weighted according to the result of the color and depth noise modeling. A degree of uncertainty and the strength of decisions, in combination with the weighting results, are used by the method to define more accurately the dynamic objects through a strong fuzzy classifier. The final stage of foreground detection is implemented with a Discrete-Time Cellular Neural Network to improve the foreground definition. Finally, the color and depth background models are updated based on a fuzzy learning rate strategy. The method was evaluated with the new SBM-RGBD database and compared against several state-of-the-art methods showing a similar or better performance considering the quantitative and qualitative evaluations.

Retracted] Painting Color Editing System Based on Virtual Reality Sensor Technology

In order to solve the problem of large color matching error in color matching system, this paper proposes a painting color editing system based on virtual reality sensing technology. Apply virtual reality technology (technology) to the proposed system design, and design the system framework based on the three‐tier architecture, which includes data access layer, business logic layer, and presentation layer. The hardware includes spectrophotometer, central processing unit, storage device, and virtual reality somatosensory interaction device. The software part takes BP neural network algorithm as the core, trains and processes the three color values, constructs the automatic matching model of painting colors according to the training results, and realizes the logical operation and analysis of the system software. The experimental results show that although there is still a certain error between the painting color matching output by the system and the actual color matching of the test samples, the maximum absolute error of the matching is relatively small, which is less than 0.05. The error between 6 groups of actual matching samples and predicted matching samples is &lt;1.5. Although the designed system has some errors with the expected color matching, the error is small, so the color difference does not affect the visual presentation effect.

Industrial Fire Detector Using Colour Detection and Tracking

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition system.

On the twilight phenomenon of the green band.

The twilight sky is usually characterized by the well-known reddish/orange colors close to the horizon and the blue colors above. However, in many cases, a green or greenish band forms between the blue and reddish parts of the sky, and it is essentially not documented in the literature. In this study, the green band phenomenon is simulated using the radiative transfer model SCIATRAN and subsequent color modeling based on the CIE color matching functions and chromaticity values. Different parameters and processes that have a potential influence are investigated. In addition, a possible contribution by airglow emissions is discussed. The simulations show that it requires just the right intensities in the blue, green, and long-wave spectral regions to produce a green color. The total ozone column has the comparatively largest influence. This study is, to the best of our knowledge, the first detailed investigation of the green band phenomenon.

Joint intensity–gradient guided generative modeling for colorization

This paper proposes an iterative score-based generative model for solving the automatic colorization problem. Although unsupervised learning methods have shown the capability to generate plausible color, inadequate exploration of detailed information and data dimensions still limit the performance of the colorization model. Considering that the number of samples in score-based generative model has influence on estimating the target gradients and the gradient map possesses important latent information of the image, the inference process of the generative modeling is conducted in joint intensity–gradient domain for colorization. Specifically, a set of intensity–gradient formed high-dimensional tensors are trained, via the score matching, to attain the gradient of data distribution in joint intensity–gradient domain. As the score function is determined, data samples are generated by means of annealed Langevin dynamics, forming an iterative colorization procedure. Furthermore, the joint intensity–gradient constraint in data-fidelity term is proposed to limit the degree of freedom within generative model at the iterative colorization stage, thus being conducive to edge-preserving colorization effect. Experimental results conveyed the remarkable performance and diversity of our proposed method.

A search for missing radio sources at z ≳ 4 using Lyman dropouts

ABSTRACT Using the Lyman Dropout technique, we identify 148 candidate radio sources at z ≳ 4–7 from the 887.5 MHz Australian Square Kilometre Array Pathfinder (ASKAP) observations of the GAMA23 field. About 112 radio sources are currently known beyond redshift z ∼ 4. However, simulations predict that hundreds of thousands of radio sources exist in that redshift range, many of which are probably in existing radio catalogues, but do not have measured redshifts, either because their optical emission is too faint or because of the lack of techniques that can identify candidate high-redshift radio sources (HzRSs). Our study addresses these issues using the Lyman Dropout search technique. This newly built sample probes radio luminosities that are 1–2 orders of magnitude fainter than known radio-active galactic nuclei (AGN) at similar redshifts, thanks to ASKAP’s sensitivity. We investigate the physical origin of radio emission in our sample using a set of diagnostics: (i) radio luminosity at 1.4 GHz, (ii) 1.4 GHz to 3.4 μm flux density ratio, (iii) Far-IR detection, (iv) WISE colour, and (v) SED modelling. The radio/IR analysis has shown that the majority of radio emission in the faint and bright end of our sample’s 887.5 MHz flux density distribution originates from AGN activity. Furthermore, ∼10 per cent of our sample are found to have a 250 μm detection, suggesting a composite system. This suggests that some high-z radio-AGNs are hosted by SB galaxies in contrast to low-z radio-AGNs, which are usually hosted by quiescent elliptical galaxies.

Simulation Based Analysis of Hierarchical Timed Colored Petri Nets Model of the Restaurant Food Serving Process

The major goal of establishing a restaurant is improved earnings making which can be jeopardized by customers' withdrawals due to lengthy ready lines. The above scenario was studied in this paper using simulation-based analysis of a Hierarchical Timed Coloured Petri Nets (HTCPN) model, which abstracted the serving operation of a renowned restaurant in the Ibadan metropolis. The restaurant was characterized by two or more servers that serially served each Customer from the multiple available servers at the four serving points. One hundred simulation runs were carried out on the HTCPN model using CPN Tools to determine the four (4) parameters,Customers' Average Flow Time (CAFT),Customers' Average Waiting Time (CAWT),Servers' Average Utilization Rate (SAUR) andCustomers' Average Queue Length (CAQL) required in serving 567, 1100, 1103, 770 and 829 customers from Monday to Friday, respectively. The model was validated by carrying out a statistical analysis t-Test between the simulated and the actual customers' flow time andservers' utilization rate at a 5% significant level. The validation results showed no significant differences between the simulated and the actual customers' average flow time andservers' utilization rate at a 5% significance level. Hence, the model can represent the restaurant under consideration.

Disentangled Image Colorization via Global Anchors

Colorization is multimodal by nature and challenges existing frameworks to achieve colorful and structurally consistent results. Even the sophisticated autoregressive model struggles to maintain long-distance color consistency due to the fragility of sequential dependence. To overcome this challenge, we propose a novel colorization framework that disentangles color multimodality and structure consistency through global color anchors, so that both aspects could be learned effectively. Our key insight is that several carefully located anchors could approximately represent the color distribution of an image, and conditioned on the anchor colors, we can predict the image color in a deterministic manner by utilizing internal correlation. To this end, we construct a colorization model with dual branches, where the color modeler predicts the color distribution for anchor color representation, and the color generator predicts the pixel colors by referring the sampled anchor colors. Importantly, the anchors are located under two principles: color independence and global coverage, which is realized with clustering analysis on the deep color features. To simplify the computation, we creatively adopt soft superpixel segmentation to reduce the image primitives, which still nicely reserves the reversibility to pixel-wise representation. Extensive experiments show that our method achieves notable superiority over various mainstream frameworks in perceptual quality. Thanks to anchor-based color representation, our model has the flexibility to support diverse and controllable colorization as well.

A method for high dynamic range 3D color modeling of objects through a color camera

This paper proposes a novel method for enhancing the dynamic range of structured-light cameras to solve the problem of highlight that occurs when 3D modeling highly reflective objects using the structured-light method. Our method uses the differences in quantum efficiency between R, G, and B pixels in the color image sensor of a monochromatic laser to obtain structured-light images of an object under test with different luminance values. Our approach sacrifices the resolution of the image sensor to increase the dynamic range of the vision system. Additionally, to enhance our system, we leverage the backgrounds of structured-light stripe pattern images to restore the color information of measured objects, whereas the background is often removed as noise in other 3D reconstruction systems. This reduces the number of cameras required for 3D reconstruction and the matching error between point clouds and color data. We modeled both highly reflective and non-highly reflective objects and achieved satisfactory results.

Raspberry pulp pasteurization: Computational fluid dynamics modeling and experimental validation of color and bioactive compound retention

AbstractThis work evaluated the heat transfer, fluid flow, and different reactions that occur during the pasteurization of raspberry pulp. The pulp was packed in glass jars and thermal processing was conducted by immersion in a water bath. The process was evaluated by multiphysics modeling, with the aim of determining the temperature distribution, flow pattern, the slowest heating zone (SHZ) location, the microbial inactivation, and the quality changes taking place during processing. In order to simulate the process accurately, a model was developed using experimentally measured product and heat transfer properties (density, thermal expansion, specific heat, thermal conductivity, apparent viscosity, and convective heat transfer coefficient) and kinetics of thermal degradation of quality parameters (color, anthocyanin, and ascorbic acid). The model was successfully validated against experimental measurements of both temperature and quality losses. After validation, the model was used to design and evaluate equivalent thermal processing conditions. Improved processes were identified that minimized the changes of color and the bioactive compounds, while maintaining product safety and stability.Practical ApplicationsThermal processing of raspberries may induce several physicochemical changes that impair their sensorial properties, and may reduce the content of nutrients and bioactive compounds. Hence, the design and optimization of heat treatments is a key tool to guarantee safety while, at the same time, minimizing detrimental side effects which involve a loss of quality. In this investigation, we assessed the actual thermo‐physical properties of raspberry pulp and the system heat transfer coefficients. Then, we developed a CFD model to predict the temperature and velocity transient profiles, the location of the SHZ, and the quality changes occurring in raspberry pulp during pasteurization. The findings of this study could be useful to food processors for optimizing pasteurization conditions in order to obtain safe and high‐quality raspberry pulp preserves, as well as for reducing processing energy requirements.

An extensible driving model for multiple grayscales colorful electrowetting displays

As a new type of reflective display technology with paper-like display performance, electrowetting display (EWD) can realize ultra-low power consumption, wide viewing angle and fast response speed. In order to apply the EWD to the large-size display field, an extensible driving model for multiple grayscales colorful EWDs was proposed in this paper. The proposed driving model consisted of a grayscale model, a colorful display model and an extensible driving model. With the grayscale model and the colorful display model, the driving waveform of the colorful grayscale display was designed based on limited output voltage levels of a general-purpose graphic driver chip. And then, the extensible driving model was used to realize the large-size display large-scale display based on a matrix of small-size EWD panels. In addition, an experimental platform, which was composed of the display panel and the driving board with a data interface, was designed based on the proposed models. Experimental results showed that it could realize an 18-bit colorful dynamic display on the extended display matrix with a resolution of 48*16. It was indicated that the display matrix could be used for multiple grayscales and colorful dynamic displays by applying the proposed driving models. The research has brought about a way of enlarging the size of the EWD, which is conducive to broadening the scope of application of EWDs.

Clear sky color modeling based on BP neural network

Daylight is a valuable resource that provides energy and luminance, as it contains changing spectrum distributions, which also carry color information and have an important impact on human vision and perception. Several sky models have been proposed for use in daylighting simulation calculations to quantify the effect of daylight. However, these models do not fully consider the color, or are modeled using the correlation of the color temperature and luminance fitting, which has color deviation, so it is difficult to realize the chromaticity-related research of daylight. Therefore, the clear sky spectral data in the Beijing area was measured and used to calculate the color coordinates of sky patches. Corresponding it with the sun position, sky patch position, and radiation data to model the sky color distribution based on backpropagation neural network method. Four-, five-, and six-variable models were established, where the five-variable model exhibited the smallest error: NMBEx = 0.52%, CVRMSEx = 3.54%, NMBEy = 0.67%, and CVRMSEy = 3.55%. However, the four- and six-variable errors were also within the specification and were easier to use. The proposed models can achieve the real-time prediction of the sky color distribution, provide a theoretical basis for daylighting design in future buildings, and guide the development of new spectrally-selective material applications and intelligent lighting control systems.

Reproducibility of Three-Dimensional Volumetric Measurement of Periocular Tumor Models.

In this study, we measured the volume of customized tumor models in the periocular area using three-dimensional (3D) stereophotogrammetry and evaluated the reproducibility of these measurements. Five tumor models of different colors and sizes were placed in different periocular positions, and 3D facial images were obtained from 68 healthy adult volunteers. Subsequently, the volumes of the tumor models were measured, and the intra- and interrater reproducibility was assessed. The gray 6mm model revealed the highest reliable measurements in both Caucasians (intra- and interrater intraclass correlation coefficients of 0.981 and 0.899, mean absolute difference of 1.446 and 3.327mm3, relative error measurement of 3.497% and 8.120%, technical error of measurement of 1.450 and 3.105mm3, and relative technical error of measurement of 3.506% and 7.580%) and Asians (0.968 and 0.844, 1.974 and 4.067mm3, 4.772% and 9.526%, 2.100 and 4.302mm3, and 5.076% and 10.076%, respectively). The highest reliability of measurements in the lateral upper eyelid (0.88 and 0.95, 4.042 and 3.626mm3, 9.730% and 9.020%, 5.714 and 3.358mm3, and 9.730% and 8.350%, respectively) and medial upper eyelid (0.81 and 0.89, 4.313 and 4.226mm3, 9.730% and 9.020%, 6.098 and 4.069mm3, and 9.730% and 8.350%, respectively) with eyes closed was evident in Caucasians, while the same trend (0.841 and 0.815, 2.828 and 3.757mm3, 9.860% and 9.840%, 4.052 and 4.308mm3, and 9.860% and 9.740%, respectively) was observed in Asians in the medial canthus with eyes closed. This study confirms, for the first time, the high reliability of periocular tumor volume measurements using 3D stereophotogrammetry, suggesting its feasibility for eyelid tumor measurement. Further trials are required to investigate its clinical use for documentation and follow-up of different eyelid tumors.

Distant Viewing of the Harry Potter Movies via Computer Vision

We present an exploratory study performing distant viewing via computer vision methods in the genre of fantasy movies. As a case study we use 10 modern fantasy movies of the Harry Potter franchise (also referred to as Wizarding World franchise). We apply methods and state-of-the-art models for color and brightness analysis, object detection, location classification as well as facial emotion recognition. We present descriptive results as well as inference statistics. Furthermore, we discuss the results and the quality of the methods for this unique use case and give examples. We were able to find significant differences in our statistical analysis in the results of the methods across the movies with the movies of the Harry Potter series getting darker and negative emotional expressions on faces becoming more frequent.

Where Perception Meets Reality: The Science of Measuring Color

Color is something that most people take for granted. A key assumption in color science is that our perceptions are similar and individual differences are small. Predictable rules, such as additive color mixing, make color modeling possible so that we can describe the richness of color in relatively low-dimensional spaces like red, green, and blue (RGB). Here, we look at how scientists define and calibrate color, various color measurement methods, and issues that arise when attempting to accurately measure and quantify color.