Color Modeling Research Articles

Color as a pictorial principle in the artistic language of Chinese painting: approaches and models of representation

The purpose of the article is to study the approaches and models of color as a pictorial principle in modern Chinese painting.
 The article is devoted to the actual problem of the study of the artistic language of painting. In Chinese art history, it has several forms of representation. However, one of the key themes is the role of color as a central pictorial principle in contemporary fine art.
 The study of the role of color in painting is based on the analysis of the formal and compositional-plastic features of artwork. At the same time, color is an important factor in the interpretation of artistic signs and symbols of a particular author. The selection of color from and a number of other elements of the artistic language is associated with the influence of the traditional artistic system in the art of China on the practice of modernization. In many professional studies, we can come across an opinion about the practical significance of tonal and color solutions for substantiating the artistic imagery of a work and its ideological and aesthetic meaning.
 The methodology. For the analysis of sources we used the method of comparative analysis and the method of typology. Visual sources are analyzed with the use of methodological, formal, compositional and figurative-stylistic analysis.
 The results. In modern Chinese studies, the problem of color is a completely independent area, which is of universal importance for the analysis of the evolution of fine art. As a visual principle, the subject matter is considered within two approaches: 1) figurative-plastic, where the central place is occupied by tonal and color features in the context of the search for form and composition; 2) emotional-figurative, where the central place is occupied by the perception of color and the interpretation of the signs and symbols of the work.
 Within the framework of the first approach, three research models dominate: 1) figurative and artistic model, which is characterized by the ability of color to change within the framework of the author’s style idea; 2) the symbolic model focuses the artist’s attention on cultural and historical references; 3) the decorative-figurative model positions the problem of color as a matter of color morphology, as well as the use of traditional decor techniques along with oil painting methods.
 Within the framework of the second approach, the sign-emotive and semantic-emotive models are the main ones. The first model represents color as a factor of the author’s sign space. The second model is a tool for studying color as a part of a sign artistic language. This artistic language regulates the specific meaning of the aesthetic essence of color (for example, red in traditional symbolism is a sign of power). In this model, the semiotic feature of color is enhanced by the emotional nature of painting techniques.
 The scientific novelty. The article substantiates the concept of color in painting as the main pictorial principle in the contemporary fine arts of China. The research approaches of Chinese art critics are summarized.

Automatic Color-Matching Model of Environmental Art Based on Visual Matching by Using IoT

In order to improve the matching effect of environmental art color automatic matching, a model of environmental art color automatic matching based on visual matching is designed. Firstly, it preprocesses the color matching of environmental art, then extracts the color space and its main color, and analyzes the color deviation. At the same time, the color model is established and the color quantization is processed, and finally the color matching is realized. The experimental results show that the matching time of the proposed model is less than that of the traditional model, and the wrong contour is less. Research Objective. A model of environmental art color-automated matching based on visual matching is developed in order to improve the matching effect of environmental art color matching. Current Challenges. Because of time constraints, the matching model still has certain drawbacks, necessitating more optimization research in the subsequent study. The main steps in the research are pretreatment before color matching of environmental art, color space and main color extraction, color deviation analysis, color modeling, color quantization processing, implementation of color matching, and experimental comparison.

The application of graphic language in animation visual guidance system under intelligent environment

Abstract With the continuous development of society, the role of the visual guidance system in animation design has also evolved and evolved in its long history, leading to the changes in the values of modern beauty. In the field of modern social and cultural design, the visual guidance system in animation design has unique regional nature and cultural influence. The visual language should correspond to the visual environment and easy to understand and be known by people. It combines animation conception and design technology to capture the cultural charm and beauty, values, and behavioral norms of people in different fields. This article studies and analyzes the visual orientation of graphic language in the design of animation visual guidance system, and injects the graphic language with orientation into its animation design, so that the animation design is more in line with the characteristics of the times. It can be more adapted to the emerging media and better convey the information transfer between the enterprise and the audience. To further understand the audience’s tendency toward elements of graphic expression, this article analyzes the subjective perceptions of the respondents on the importance of color selection, calligraphy fonts, graphic expression, and modeling meaning. The results of the study showed that the respondents aged 21–35 paid more attention to the choice of graphic colors, and the highest number was 69.

Merging cloned Alloy models with colorful refactorings

Likewise to code, clone-and-own is a common way to create variants of a model, to explore the impact of different features while exploring the design of a software system. Previously, we have introduced Colorful Alloy , an extension of the popular Alloy language and toolkit to support feature-oriented design, where model elements can be annotated with feature expressions and further highlighted with different colors to ease understanding. In this paper we propose a catalog of refactoring laws for Colorful Alloy models, and show how they can be used to iteratively merge cloned Alloy models into a single feature-annotated colorful model, where the commonalities and differences between the different clones are easily perceived, and more efficient aggregated analyses can be performed. We then show how these refactorings can be composed in an automated merging strategy that can be used to migrate Alloy clones into a Colorful Alloy SPL in a single step. The paper extends a conference version [1] by formalizing the semantics and type system of the improved Colorful Alloy language, allowing the simplification of some rules and the evaluation of their soundness. Additional rules were added to the catalog, and the evaluation extended. The automated merging strategy is also novel. • Alloy is a popular specification language for validating software design. • Colorful Alloy is an extension of Alloy to support feature-oriented design. • Refactoring rules can be used to merge legacy clones into a single annotated model. • A strategy is proposed to automatically apply such refactorings in clone migration.

Point and Line to Surface: The Geometric Elements of Display Color Modeling

Color appearance is multidimensional, and color space has been a useful geometric representation for display modeling and optimization. However, the three fundamental attributes of color, i.e., brightness, saturation, and hue, have not found their singly corresponding physical correlates. Changes along one physical dimension interfere with other color attributes, which has been a deficiency of the existing color spaces, particularly prevalent for high-dynamic-range and wide-color-gamut displays. This paper describes how we set out to develop independent color scales for each attribute. Based on both psychophysical experiments and computational modeling, the surfaces/lines of equal brightness/saturation, as well as the boundaries between surface versus illumination color modes, have been characterized. Furthermore, the independent relations between those new scales have been quantitatively evaluated. Those results promise a new color representation that is more intuitive and efficient for color controls in displays.

CASR-Net: A color-aware super-resolution network for panchromatic image

Spatial resolution is the ability to distinguish the spatial details of remote sensing images, and high spatial resolution images are conducive to object recognition and visual interpretation. Spectral resolution is the ability to distinguish the spectral details of the ground objects in remote sensing images, and high spectral resolution images are of great significance to the classification and recognition of objects in remote sensing images. The image super-resolution model is used to enhance the spatial resolution of remote sensing image, but it cannot enhance the spectral resolution, while the image colorization model can increase the number of channels by predicting chromatic channels for the input image, thereby improving spectral resolution. In this paper, a color-aware super-resolution network that combines image colorization and super-resolution ideas is designed to improve the spectral and spatial resolution of panchromatic images. The color-aware super-resolution network mainly contains color-aware block and spatial-aware block, color-aware block is presented to predict color information for panchromatic images to improve the spectral resolution, meanwhile, spatial-aware block is used to restore the texture details for panchromatic images to improve the spatial resolution. The trained color-aware super-resolution network only needs to input panchromatic images to generate images with more spectral information and higher spatial resolution than input images. Extensive experiments demonstrate that our color-aware super-resolution network has a good performance in image colorization and super-resolution, and experimental results show that compare with some existing excellent image colorization methods and super-resolution methods, our method is excellent in objective indicators and visual effects.

Automatic colorization of natural images using deep learning

An approach based on deep learning for automatic colorization of image with optional userguided hints. The system maps a grey-scale image, along with, user hints” (selected colors) to an output colorization with a Convolution Neural Network (CNN). Previous approaches have relied heavily on user input which results in non-real-time desaturated outputs. The network takes user edits by fusing low-level information of source with high-level information, learned from large-scale data. Some networks are trained on a large data set to eliminate this dependency. The image colorization systems find their applications in astronomical photography, CCTV footage, electron microscopy, etc. The various approaches combine color data from large data sets and user inputs provide a model for accurate and efficient colorization of grey-scale images. Keywords—image colorization; deep learning; convolutional neural network; image processing.

Visual means of artistic expression of Italian social advertising

This article is devoted to the consideration of visual means of artistic expression in visual and cinematic social advertising in Italy. The research material was videos from the Internet archives of non-profit organizations and advertising agencies. The relevance of the research is due to the high significance of social advertising in modern society, and the novelty is due to the lack of works devoted to the study of visual means of artistic expression within the socio—advertising discourse of Italy. Within the framework of the study, methods of analyzing visual artistic images used in Italian social advertising were applied. An analogy was drawn between stylistic figures related to the field of philology and artistic and pictorial forms of the material under study. As the analysis has shown, the most popular visual means of artistic expression is metaphor; the author has identified a number of models of metaphorical transfers, on which most of the plots of social advertising are based, among which a special place is occupied by models of color and zoonymic metaphors. In a significant part of the videos, such means of visual artistic expression as allegory, personification, depersonification, hyperbole, allusion are also involved. Important conclusions are drawn about the cultural peculiarities of the use of these visual stylistic figures. A special contribution of the author is the development of a classification of metaphorical models of Italian social advertising, some of which may be applicable in Russia due to their universality.

Analysis of the common model error on velocity field under Colored noise model by GPS and InSAR: A case study in the Nepal and everest region

The accuracy of the velocity field will be affected by the noise model and common mode errors through GPS time series analysis. In order to analyze the influence of these two factors on the accuracy of the velocity field, two kinds of data are used, including the three-year observation from 20 permanent GPS stations with high spatial correlation in the Everest, which is about 650 km from north to south and 1068 km from east to west, and three-year 80 ascending images and 141 descending images from sentinel-1A, which are processed by GAMIT/GLOBK software and Small Baseline Subset-Interferometric Synthetic Aperture Radar method (SBAS-InSAR), respectively. The vertical deformation rate is solved by time series analysis through a self-made adaptive algorithm. In the analysis, the linear change rate, period, half period coefficient, and residual sequence of all stations are solved by using James L. Davis periodic model. The noise type of residual sequence is analyzed by the power spectrum model. The spatio-temporal correlated noise, Common Mode Error (CME), is extracted by the Principal Component Analysis (PCA) and Karhunen-Loeve (KLE) methods. The results show that noises can be best described by “flicker noise + white noise” model. After the removal of CME, the R2 estimates of all stations are above 0.8, with RMS value of velocity field decreasing from 1.428 mm/yr to 1.062 mm/yr and 1.063 mm/yr to 0.815 mm/yr, in N and E directions, respectively, indicating that the influence of CME can't be ignored in the extraction of the high-precision velocity field in the Nepal and Everest region.

Image naturalness model based on prototypical memory colors (sky, grass, and skin)

AbstractImage naturalness is a potent attribute to enhance image quality, and it is related to people's perception of memory colors. Many previous experiments aimed to locate the memory colors on different media. Their findings indicate that the memory colors obtained vary between color patches and images and between experimental techniques. However, there were no satisfying models with appropriate memory color centers to scale the image naturalness. This study generated an image database including three prototypical memory colors images (sky, grass, and skin) and their local processed images. First, visual naturalness and preference data were collected from a psychophysical experiment. Then, the distribution of naturalness followed by memory color positions was analyzed. Based on the distribution pattern, the ellipsoid models for each prototypical memory color were established. The results from the present memory color centers were compared with those from the other studies. Results show that the present color centers always give better prediction accuracy in image naturalness models. Finally, an image preference model was developed based on the image naturalness and colorfulness.

A back‐propagation neural network model using hyperspectral imaging applied to variety nondestructive detection of cereal

AbstractCereal crops play an important role in preventing chronic diseases and regulating human functions due to their rich phytochemicals. However, an increasing number of cases of cereal‐crop adulteration are occurring, affecting the nutrition of food and threatening food safety. Therefore, this study used hyperspectral imaging (HSI) and a back‐propagation neural network (BPNN) to establish the variety identification model of different grain shapes, colors, and sizes. This model can conduct a quantitative and qualitative analysis of samples rapidly and nondestructively, because HSI can collect spectral information and spatial information of samples. Meanwhile, the Watershed algorithm was used to identify cereal varieties by particles, and the model identification performance was verified by the unlabeled test sets. The results show that the data extraction success rate of the new watershed algorithm reached 98%, and the comprehensive identification accuracy of the model reached 90%. In addition, the cereal in the training set can be changed to identify other cereal crops, thereby providing a method of rapid and nondestructive adulteration detection of cereals.Practical ApplicationsCereal crops play an important role in preventing chronic diseases and regulating human functions due to their rich phytochemicals. However, an increasing number of cases of cereal‐crop adulteration are occurring, affecting the nutrition of food and threatening food safety. Therefore, this study used hyperspectral imaging (HSI) and a back‐propagation neural network (BPNN) to establish the variety identification model of different grain shapes, colors, and sizes. This model can conduct a quantitative and qualitative analysis of samples rapidly and nondestructively, because HSI can collect spectral information and spatial information of samples at the same time. The spectral information was used for qualitative analysis, while spatial information was used for quantitative analysis, so this model can realize the rapid and nondestructive detection of different varieties of cereals. And we can also change the training‐set data to realize the variety identification of other varieties of crops, which provides guidance for the method detecting the adulteration of cereal crops.

V72 - Colored perfusion areas-based 3D virtual models: The Rainbow Kidney as a new tool to optimize the clamping strategy during robot-assisted partial nephrectomy

V72 - Colored perfusion areas-based 3D virtual models: The Rainbow Kidney as a new tool to optimize the clamping strategy during robot-assisted partial nephrectomy

Quantitative structure–activity relationship(QSAR) models for color and COD removal for some dyes subjected to electrochemical oxidation

ABSTRACT Electrochemical oxidation is an efficient method for the destruction of dyes in wastewater streams. The experimental conditions during electrochemical oxidation (EO) and molecular structure of a dye greatly influence the extent of degradation. The extent of degradation for a variety of dyes by EO can be predicted conveniently by the use of Quantitative structure–activity Relationship (QSAR) models. An abundant amount of published data on dye degradation by EO using highly variable experimental conditions lies unutilized to prepare QSAR models. In this study, an effort is made to use published experimental data on EO of aqueous dyes after applying an easy method of normalization, to prepare QSAR models for percent color and COD removal. Normalized color and COD removal were obtained by multiplying the reported removal by volume of reactor and concentration of dye; and divided by total current passed and the time of electrolysis. More than 15 molecular descriptors were computed using Schrodinger-suit 2018-3. The multiple linear regression (MLR) approach was used to develop normalized color and COD removal models. The quantum chemical descriptors: highest occupied molecular orbital energy (HOMO) and lowest unoccupied molecular orbital energy (LUMO), polar surface area (PSA), hydrogen bond donor count (HBD), and number of atoms were found significant. The statistical indices: goodness-of-fit, R2 > 0.75, and internal and external validations, Q2 LOOCV and Q2 ext, > 0.5, satisfied the criteria for predictive models and indicated that the method of normalization used in this study is adequate. Developed QSAR models are quite simple, interpretable, and transparent.

CNN and Fuzzy Rules Based Text Detection and Recognition from Natural Scenes

In today’s real world, an important research part in image processing is scene text detection and recognition. Scene text can be in different languages, fonts, sizes, colours, orientations and structures. Moreover, the aspect ratios and layouts of a scene text may differ significantly. All these variations appear assignificant challenges for the detection and recognition algorithms that are considered for the text in natural scenes. In this paper, a new intelligent text detection and recognition method for detectingthe text from natural scenes and forrecognizing the text by applying the newly proposed Conditional Random Field-based fuzzy rules incorporated Convolutional Neural Network (CR-CNN) has been proposed. Moreover, we have recommended a new text detection method for detecting the exact text from the input natural scene images. For enhancing the presentation of the edge detection process, image pre-processing activities such as edge detection and color modeling have beenapplied in this work. In addition, we have generated new fuzzy rules for making effective decisions on the processes of text detection and recognition. The experiments have been directedusing the standard benchmark datasets such as the ICDAR 2003, the ICDAR 2011, the ICDAR 2005 and the SVT and have achieved better detection accuracy intext detection and recognition. By using these three datasets, five different experiments have been conducted for evaluating the proposed model. And also, we have compared the proposed system with the other classifiers such as the SVM, the MLP and the CNN. In these comparisons, the proposed model has achieved better classification accuracywhen compared with the other existing works.

A TCPN-based model for testing distributed systems with timing constraints

This paper focuses on extending the basis of distributed test to address the testing process for time-sensitive distributed systems. In this context, we present an alternative approach to manage the problems that occur in this area, commonly referred to as controllability, observability and synchronisation issues. The key point of the proposed study is to define the activities of each tester through a set of timed distributed testing rules. Thus, we present our algorithm for the generation of such rules. Each rule is handled as a data structure comprising the data to be sent or received, the guard to be controlled and the set of clocks to be updated at the end of each transition. Afterwards, we show how we can use a timed coloured Petri nets model to cope with the complex tasks of testers' monitoring in the distributed test context. The simulation results revealed the effectiveness of our approach in providing correct execution of the system actions but also show how the response time of each tester can be improved by considering the temporal constraints. Indeed, the test becomes non-blocking and stops immediately by returning a false verdict if the temporal constraints have not been met.

КОЛОРИЗАЦИЯ ЧЕРНО-БЕЛЫХ ФОТОГРАФИЙ НА ОСНОВЕ МОДЕЛИ НЕЙРОННОЙ СЕТИV. D. Pivovarov

The article discusses the development of a neural network model for colorization of black and white images. The main issues that arise when designing a neural network model for solving this problem are described. The choice of switching to work with the LAB color space from RGB is justified. The GAN architecture is described. The U-Net architecture is described. Models of the generator and discriminator are constructed. The tools used in the development of the neural network are described.

A Variation-Based Nighttime Image Dehazing Flow With a Physically Valid Illumination Estimator and a Luminance-Guided Coloring Model

A Variation-Based Nighttime Image Dehazing Flow With a Physically Valid Illumination Estimator and a Luminance-Guided Coloring Model

Self-Supervised Color-Concept Association via Image Colorization.

The interpretation of colors in visualizations is facilitated when the assignments between colors and concepts in the visualizations match human's expectations, implying that the colors can be interpreted in a semantic manner. However, manually creating a dataset of suitable associations between colors and concepts for use in visualizations is costly, as such associations would have to be collected from humans for a large variety of concepts. To address the challenge of collecting this data, we introduce a method to extract color-concept associations automatically from a set of concept images. While the state-of-the-art method extracts associations from data with supervised learning, we developed a self-supervised method based on colorization that does not require the preparation of ground truth color-concept associations. Our key insight is that a set of images of a concept should be sufficient for learning color-concept associations, since humans also learn to associate colors to concepts mainly from past visual input. Thus, we propose to use an automatic colorization method to extract statistical models of the color-concept associations that appear in concept images. Specifically, we take a colorization model pre-trained on ImageNet and fine-tune it on the set of images associated with a given concept, to predict pixel-wise probability distributions in Lab color space for the images. Then, we convert the predicted probability distributions into color ratings for a given color library and aggregate them for all the images of a concept to obtain the final color-concept associations. We evaluate our method using four different evaluation metrics and via a user study. Experiments show that, although the state-of-the-art method based on supervised learning with user-provided ratings is more effective at capturing relative associations, our self-supervised method obtains overall better results according to metrics like Earth Mover's Distance (EMD) and Entropy Difference (ED), which are closer to human perception of color distributions.

Predation on Common Wall Lizards: Survival Probabilities of Melanic Individuals

For estimation of predation plasticine models of prey animals are often used, because the soft material preserves imprints left by predators. We assumed that melanic common wall lizards (Podarcis muralis) disappear by selective predation faster than cryptic individuals and habitat features have important role in this process. We studied the survival probabilities of cryptic and melanic colored plasticine common wall lizard models in habitats with different background coloration on selected places near the city of Pécs (south Hungary), where melanic common wall lizards had been observed earlier. Contrary to our expectations the daily survival rates of melanic plasticine common wall lizards were somewhat higher in all three locations (sandstone quarry, stone wall, coal pit) than those of the cryptic ones, but these differences were not significant. Predators were mostly mammals, which left more marks on plasticine models than birds, but we could not show a preference of the body parts of prey. We concluded that rare occurrence of melanic common wall lizards in habitats near the city of Pécs is not due to predation pressure.

Fully automatic image colorization based on semantic segmentation technology.

Aiming at these problems of image colorization algorithms based on deep learning, such as color bleeding and insufficient color, this paper converts the study of image colorization to the optimization of image semantic segmentation, and proposes a fully automatic image colorization model based on semantic segmentation technology. Firstly, we use the encoder as the local feature extraction network and use VGG-16 as the global feature extraction network. These two parts do not interfere with each other, but they share the low-level feature. Then, the first fusion module is constructed to merge local features and global features, and the fusion results are input into semantic segmentation network and color prediction network respectively. Finally, the color prediction network obtains the semantic segmentation information of the image through the second fusion module, and predicts the chrominance of the image based on it. Through several sets of experiments, it is proved that the performance of our model becomes stronger and stronger under the nourishment of the data. Even in some complex scenes, our model can predict reasonable colors and color correctly, and the output effect is very real and natural.