Color Modeling Research Articles

Enhancing Accuracy of Flame Equivalence Ratio Measurements: An Attention-Based Convolutional Neural Network Approach for Overcoming Limitations in Traditional Color Modeling.

This paper addresses the inherent limitations in traditional color modeling techniques for measuring the flame equivalence ratio (Φ), particularly focusing on the subjectivity involved in threshold settings and the challenges posed by uneven 2D color distribution. To overcome these issues, this study introduces an attention-based convolutional neural network (ACN) model, a novel approach that transcends the conventional reliance on B/G color features (Tf). The ACN model leverages adaptive feature extraction, augmented by a spatial attention mechanism, to more effectively analyze flame images. By amplifying key features, autonomously minimizing background noise, and standardizing variations in color distribution, the ACN model in this experiment achieved a prediction accuracy of 99%, with a 76% reduction in error rate compared to the original model, significantly improving the accuracy and objectivity of flame Φ measurement. This method marks a substantial development in the precision and reliability of flame analysis.

NEUROPSYCHOLOGICAL ASPECTS OF EXECUTIVE FUNCTIONS IN THE SPATIAL CONSTRUCTION OF COLOR MODELS IN STUDENTS WITH AND WITHOUT LEARNING DIFFICULTIES

The effectiveness of the spatial construction of models is directly related to the degree of internal spatio-temporal structuring and organization which, in turn, depends on the degree of the functional optimization of the subjects of the action. The objective of this study is to detect neuropsychological differences in executive functions, between students with and without learning difficulties, in terms of attempts and time spent performing spatial construction tasks. The research hypothesis states that there are differences in the number of attempts and time spent when performing spatial tasks, with greater expressiveness in students with learning difficulties. In methodology, the resource focused on the neuropsychological instrument of spatial construction used colored models based on the manipulation of four Kohs cubes; as well as neuropsychological observation and psychological interview. The results show that at most ages the tendency is for students with learning difficulties to make a greater number of attempts and spend more time performing tasks related to spatial construction tests, compared to students without learning difficulties. In conclusion, it is inferred that students with learning difficulties tend to present internal spatio-temporal structuring and organization deficits, which result from deficits in the operability of executive functions.

Colored vertex models and Iwahori Whittaker functions

We give a recursive method for computing all values of a basis of Whittaker functions for unramified principal series invariant under an Iwahori or parahoric subgroup of a split reductive group G over a nonarchimedean local field F. Structures in the proof have surprising analogies to features of certain solvable lattice models. In the case G=GLr\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G=\ extrm{GL}_r$$\\end{document} we show that there exist solvable lattice models whose partition functions give precisely all of these values. Here ‘solvable’ means that the models have a family of Yang–Baxter equations which imply, among other things, that their partition functions satisfy the same recursions as those for Iwahori or parahoric Whittaker functions. The R-matrices for these Yang–Baxter equations come from a Drinfeld twist of the quantum group Uq(gl^(r|1))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U_q(\\widehat{\\mathfrak {gl}}(r|1))$$\\end{document}, which we then connect to the standard intertwining operators on the unramified principal series. We use our results to connect Iwahori and parahoric Whittaker functions to variations of Macdonald polynomials.

Increasing airspace capacity by improving ATCo’s efficiency through an innovative handover mechanism

The current lack of airspace capacity poses a major challenge to a sustainable and efficient air transport system, especially as future demand increases. Recent efforts to increase airspace capacity have focused on airspace sectorization. The digitization of Air Traffic Management (ATM) holds promise for overcoming current inefficiencies arising from the spatial fragmentation of airspace. While research has explored digitized ATM services to improve airspace capacity, there has been a notable gap in addressing spare capacity through enhancing synergies between adjacent sectors. This work proposes the application of an early handover (EHO) mechanism based on the spatio-temporal interdependencies among sectors and aircraft. Essentially, the mechanism involves alleviating overloaded sectors by redistributing peak demand to neighboring ones. This alleviation results in a reduction of the dedicated resources required to meet demand, facilitating the consolidation of sectors into larger units. Ultimately, this consolidation enhances airspace efficiency by minimizing the ATCo resources necessary for ATM. A simple and fast heuristic is proposed to select the aircraft for early handover to the air traffic controller of another sector, together with a specific schedule. A Colored Petri Net model is implemented to formalize the dynamic spatio-temporal interdependencies among adjacent sectors and to illustrate the EHO applicability using an academic exercise for illustration purposes. Afterwards, a simulation study based on real European airspace data is employed to illustrate and validate the mechanism. Finally, a discussion on the potential impacts of adopting the mechanism in real-life applications and lines of future research are provided.

Continual few-shot patch-based learning for anime-style colorization

The automatic colorization of anime line drawings is a challenging problem in production pipelines. Recent advances in deep neural networks have addressed this problem; however, collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines. To overcome this obstacle, we propose a new patch-based learning method for few-shot anime-style colorization. The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings. We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists. The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily incorporated within existing production pipelines. We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.

The Complexity of Finding Fair Many-to-One Matchings

We analyze the (parameterized) computational complexity of “fair” variants of bipartite many-to-one matching, where each vertex from the “left” side is matched to exactly one vertex and each vertex from the “right” side may be matched to multiple vertices. We want to find a “fair” matching, in which each vertex from the right side is matched to a “fair” set of vertices. Assuming that each vertex from the left side has one color modeling its “attribute”, we study two fairness criteria. For instance, in one of them, we deem a vertex set fair if for any two colors, the difference between the numbers of their occurrences does not exceed a given threshold. Fairness is, for instance, relevant when finding many-to-one matchings between students and colleges, voters and constituencies, and applicants and firms. Here colors may model sociodemographic attributes, party memberships, and qualifications, respectively. We show that finding a fair many-to-one matching is NP-hard even for three colors and maximum degree five. Our main contribution is the design of fixed-parameter tractable algorithms with respect to the number of vertices on the right side. Our algorithms make use of a variety of techniques including color coding. At the core lie integer linear programs encoding Hall like conditions. We establish the correctness of our integer programs, based on Frank’s separation theorem [Frank, Discrete Math. 1982]. We further obtain complete complexity dichotomies regarding the number of colors and the maximum degree of each side.

Study on the Translation and Application of Hakka Cultural Symbols in Heyuan Tourist Food Packaging

According to the present situation of tourism food packaging design in Heyuan area, the problems such as lack of creativity, lack of cultural connotation and single function are found out. Color elements, modeling elements and decorative elements from Hakka culture are extracted and translated into tourist food packaging by means of design semiotics, and digital media technology is integrated to focus on functional innovation of food packaging. The design strategy of Hakka cultural translation in Heyuan tourist food packaging is proposed: “Modeling transplantation and theme narrative” highlight regional characteristics. “Visual presentation and emotional resonance” gives spiritual connotation. “Hakka culture and technology integration” highlights cultural inheritance, and applies it to the design of tourism food packaging, which is conducive to the dissemination and development of traditional culture. It helps to shape a good brand image, and further realizes the cultural value and economic value of tourism food packaging.

Colorizing Monochromatic Radiance Fields

Though Neural Radiance Fields (NeRF) can produce colorful 3D representations of the world by using a set of 2D images, such ability becomes non-existent when only monochromatic images are provided. Since color is necessary in representing the world, reproducing color from monochromatic radiance fields becomes crucial. To achieve this goal, instead of manipulating the monochromatic radiance fields directly, we consider it as a representation-prediction task in the Lab color space. By first constructing the luminance and density representation using monochromatic images, our prediction stage can recreate color representation on the basis of an image colorization module. We then reproduce a colorful implicit model through the representation of luminance, density, and color. Extensive experiments have been conducted to validate the effectiveness of our approaches. Our project page: https://liquidammonia.github.io/color-nerf.

Applying Colorful Scramble Learning Model to Increase Students’ Critical Thinking and Learning Results

This study aims to increase students' critical thinking and learning results through the application of the Colorful Scramble Learning Model. The subjects of this study were students of class VII H SMP N 1 Pontianak. The objects of this research are the increasing of students’ critical thinking and learning results in learning English. The method of data collection is through observation of teaching and learning activities, students’ assessment for learning results, and documentations. The data were analysed qualitatively using Likert Scale and quantitatively using IBM SPSS Statistics 26. The result of this study indicates that the Colorful Scramble Learning Model can increase critical thinking and learning results of students in class VII H SMP N 1 Pontianak in learning. Keywords: Critical Thinking, Learning Results, Colourful-Scramble Learning Model.

Low-Cost Training of Image-to-Image Diffusion Models with Incremental Learning and Task/Domain Adaptation

Diffusion models specialized in image-to-image translation tasks, like inpainting and colorization, have outperformed the state of the art, yet their computational requirements are exceptionally demanding. This study analyzes different strategies to train image-to-image diffusion models in a low-resource setting. The studied strategies include incremental learning and task/domain transfer learning. First, a base model for human face inpainting is trained from scratch with an incremental learning strategy. The resulting model achieves an FID score almost equivalent to that of its batch learning equivalent while significantly reducing the training time. Second, the base model is fine-tuned to perform a different task, image colorization, and, in a different domain, landscape images. The resulting colorization models showcase exceptional performances with a minimal number of training epochs. We examine the impact of different configurations and provide insights into the ability of image-to-image diffusion models for transfer learning across tasks and domains.

AnimeDiffusion: Anime Diffusion Colorization.

Being essential in animation creation, colorizing anime line drawings is usually a tedious and time-consuming manual task. Reference-based line drawing colorization provides an intuitive way to automatically colorize target line drawings using reference images. The prevailing approaches are based on generative adversarial networks (GANs), yet these methods still cannot generate high-quality results comparable to manually-colored ones. In this article, a new AnimeDiffusion approach is proposed via hybrid diffusions for the automatic colorization of anime face line drawings. This is the first attempt to utilize the diffusion model for reference-based colorization, which demands a high level of control over the image synthesis process. To do so, a hybrid end-to-end training strategy is designed, including phase 1 for training diffusion model with classifier-free guidance and phase 2 for efficiently updating color tone with a target reference colored image. The model learns denoising and structure-capturing ability in phase 1, and in phase 2, the model learns more accurate color information. Utilizing our hybrid training strategy, the network convergence speed is accelerated, and the colorization performance is improved. Our AnimeDiffusion generates colorization results with semantic correspondence and color consistency. In addition, the model has a certain generalization performance for line drawings of different line styles. To train and evaluate colorization methods, an anime face line drawing colorization benchmark dataset, containing 31,696 training data and 579 testing data, is introduced and shared. Extensive experiments and user studies have demonstrated that our proposed AnimeDiffusion outperforms state-of-the-art GAN-based methods and another diffusion-based model, both quantitatively and qualitatively.

Colored Bosonic models and matrix coefficients

Colored Bosonic models and matrix coefficients

Hyperbolic decision-making based on RBF for diabetic/COVID-19 iris

The Iris is the most complex tissue of the body that shows thousands of nerves, blood vessels, muscles, and other tissues. This study presents techniques for searching points on the iris by visual query language based on the Gaussian radial basis function (GRBF) and Hyperbolic decision tree; there are several types of iris belonging to diabetic patients; we consider the behavior of COVID-19 on diabetes; our solution detects similarities in symptoms for two different conditions. It can estimate signs of new patterns of diseases on the next cycle of the time interval of illness for virus mutation. It can match the zones in the iris by Delaunay triangulation in d3. js. Then it can design colored models with the same color for spatial diseases. The best algorithm is used for community detection for a group of signs clustered and reducing the error with K-NN by neo4j on the Cypher platform. It detects congestion of infection and inflammation of chronic diseases as a background disease; Covid-19 disorder is illustrated with a centrality algorithm based on a shared relationship for two diseases graph. Our techniques calculate the accuracy of our query design for the target shared symptoms model for new diseases. We reduced complexity algorithms and search time by decreasing the redundancy of data points. We provide the abstract layer in the target model to reduce the dimensionality of data. We present a model from a graph with a description of knowledge that it can generate to determine the target user. Algorithms optimize this model. This model can map the points of the circle into the query and hyperbolic tree, and decide for their classification in the next mutation of diseases. Our solution improved the performance by about 97% in memory usage and time-consuming of search data in complex mixed diseases with different colors and signs.

The Element of Physical Reality Hidden in the Letter of Malus to Lancret in 1800 can Solve the EPR Paradox (Malus Thermochromatic Loophole)

It is extremely difficult to discover an element of physical reality that might solve the “spooky action at a distance” formulated by Einstein-Podolsky-Rosen known as the EPR paradox. One very promising candidate was recently discovered in the letter of Malus addressed to Lancret in 1800. In this letter Malus (the discoverer of the polarization of light in 1808) modelled color as the composition of light and “caloric”. In the modern notation we can formulate the color and heat of polarized photons as the ratio of the ordinary and extraordinary wavefronts of that polarized photon in the Descartes’ model of colors caused by the rotation of spin-orbit of photons. Laser photons pass through the half waveplate where they get color and “heat content”, and then in the process of the spontaneous parametric down conversion, they create two entangled photons. In the pleochroic polarizers of Alice and Bob entangled colored photons modify individually their colors and their “heat content”. Pleochroism from Greek words πλÅLεω (pléon) and χρωμα (khrôma) means “more colors” and in the geological analysis describes dependence of color variation on the orientations of polarizer, analyzer, and the sample. This independent local color change in Alice and Bob polarizers can be mathematically described by the haversine and havercosine formulae. The havercosine describes the probability of a particle to stay on the same latitude. The haversine describes the probability of a particle to stay on the same longitude. In this model the “spooky action at a distance” is interpreted as the “local pleochroism” of entangled photons. This model can be further tested in the “Herschel-type” experiments where the polarized photons heat thermometers in the dependence of their “caloric” content. Bell four states mathematically describe the entanglement of two particles but without the physical interpretation based on the local pleochroism.

Modeling of Grapefruit External Color as Affected by Drought Stress

Grapefruit are well-adapted to arid and warm climatic conditions, but well-irrigated trees usually produce better-quality fruits. Because water is a major component of the fruits, there is a strong relationship between drought stress and fruits quality traits such as fruits size, external fruits color, and juice quality. The object of this study was to develop a computer model to predict postharvest external grapefruit color as a function of drought stress. During model development, drought stress was quantified using a concise water balance model based on crop evapotranspiration, precipitation, and irrigation. Data collected from Murcia, Spain, during the 2007 and 2008 growing seasons were used for model development, and the model was optimized by comparing model predictions and observations for each growing season. The root mean square error and Nash and Sutcliffe coefficient of efficiency (NSE) were used to evaluate model performance. Then, the model was evaluated with independent data collected from Florida during the 2005–06 growing season. A second-order polynomial relationship was found between external fruits color and drought stress, with less drought stress resulting in better external fruits color. Model optimization revealed good model performance for predicting external fruits color in Murcia, with NSE values of 0.975 and 0.979 for the 2007 and 2008 growing seasons, respectively. Model evaluation with the data from Florida showed that model predictions were reliable, with a NSE value of 0.984. A robust model to predict external grapefruit color as affected by drought stress was developed during the present study and could be potentially applied to supply information for suitable irrigation management of various grapefruit cultivars grown under different climatic conditions. Model performance could be confirmed by future research with data collection during further multiple seasons for different cultivars and a range of climatic conditions.

Thermal infrared image coloring method and evaluation method based on edge consistency

Thermal imaging cameras have broad application prospects due to the characteristics of their shooting principles. With the development of deep learning technology, thermal infrared grayscale images can be transformed into RGB color images through a grayscale image colorization model, which provides more application possibilities for thermal infrared grayscale images. However, thermal infrared grayscale images are more difficult to color than visible and near-infrared grayscale images due to their lack of pixel-level matching visible light color map data. Therefore, we propose an unsupervised learning scheme based on CycleGAN, which reduces the loss of effective edge information and suppresses the generation of abnormal edge information during the colorization process by increasing the self-supervision of edge information in the cycle process. In addition, we propose a new edge similarity indicator Edge Consistency Index Measure (ECIM), which evaluates the quality of the coloring results from the perspective of edge consistency before and after colorization. By comparing with the existing methods, we show that our method is better than the existing methods in coloring effect, and the proposed ECIM can well describe the consistency of the edges before and after colorization.

Palette‐Based and Harmony‐Guided Colorization for Vector Icons

AbstractColorizing icon is a challenging task, even for skillful artists, as it involves balancing aesthetics and practical considerations. Prior works have primarily focused on colorizing pixel‐based icons, which do not seamlessly integrate into the current vector‐based icon design workflow. In this paper, we propose a palette‐based colorization algorithm for vector icons without the need for rasterization. Our algorithm takes a vector icon and a five‐color palette as input and generates various colorized results for designers to choose from. Inspired by the common icon design workflow, we developed our algorithm to consist of two steps: generating a colorization template and performing the palette‐based color transfer. To generate the colorization templates, we introduce a novel vector icon colorization model that employs an MRF‐based loss and a color harmony loss. The color harmony loss encourages the alignment of the resulting color template with widely used harmony templates. We then map the predicted colorization template to chroma‐like palette colors to obtain diverse colorization results. We compare our results with those generated by previous pixel‐based icon colorization methods and validate the effectiveness of our algorithm by evaluations in both qualitative and quantitative measurements. Our method enables icon designers to explore diverse colorization results for a single icon using different color palettes while also efficiently evaluating the suitability of a color palette for a set of icons.

Colored fermionic vertex models and symmetric functions

Colored fermionic vertex models and symmetric functions

Preserving Colour Fidelity in Photogrammetry—An Empirically Grounded Study and Workflow for Cultural Heritage Preservation

In this paper, a study is performed in order to achieve a process that successfully respects the colour integrity of photogrammetry models of cultural heritage pieces. As a crucial characteristic of cultural heritage documentation, the colour of the pieces—as a valuable source of information—needs to be properly handled and preserved, since digital tools may induce variations in its values, or lose them to a degree. Different conditions for image acquisition schemes, RGB value calculation, calibration and photogrammetry have been combined and the results measured, so the adequate procedure is found. Control over all colour transformations is enforced, with blending operations during the texture generation process being the only unpredictable step in the pipeline. It is demonstrated that an excellent degree of colour information preservation can be achieved when applying said control on the factors of acquisition and colour digitalization, inclusive deciding their parameters. This paper aims to serve as guidelines of a correct handling of colour information and workflow so cultural heritage documentation can be performed with the highest degree of colour fidelity, covering the gap of non-existing standard procedure or conditions to perform an optimum digital cultural heritage colour modelling.

Deep Learning-Based Machine Color Emotion Generation

This paper investigates generating machine color emotion through deep learning. The grayscale image colorization model's training process resembles human memory color. Sixty images were recolored and quality evaluated to explore machine generated color impressions. Six experimental samples were recolored under D65, A, CWF, and TL84 light sources. Changes in lightness, chroma, and hue angle compared the original and colorized images, exploring light source effects on machine color perception. Analyzing differences in coloring results within the CIEL* a* b* color space for pixels with equal grayscale verified machine color emotion generation. Results show the machine learns to form color impressions from samples. Different light source color temperatures impact color prediction accuracy. The machine accurately colors images based on semantic context, demonstrating spontaneous color emotion generation through deep learning. This research positively contributes to the development of intelligent devices with color emotion.