Color Modeling Research Articles

Fine‐grained semantic ethnic costume high‐resolution image colorization with conditional GAN

Grayscale image colorization, especially for ethnic costume images, is highly challenging due to its rich and complex color features. The existing image colorization methods usually take the costume image as a whole in practical applications that lead to the ignorance of the semantic information of different parts of the costume. It is known that each part's color distribution of the ethnic costume is different. So, the color mapping of other parts is also diverse, which is determined by distinctive ethnic characteristics. This study introduces fine-grained level semantic information and proposes a high-resolution image colorization model for ethnic costumes targeting enhancement, inspired by semantic-level colorization. The semantic information of different regions of ethnic costumes has a significant impact on the performance of the coloring task. Using Pix2PixHD as the backbone network, we create a new network architecture that maintains color distribution correspondence and spatial consistency of costume images using fine-grained semantic information. In our network, we take the splice result of fine-grained semantic for ethnic costume and grayscale image as the conditions and then feed them into the generative adversarial networks. We also discuss and analyze the influences of the grayscale channel and fine-grained semantics on discriminator. Extensive experiments demonstrate that our method performs well compared with other state-of-the-art automatic colorization methods.

Distributed architecture for a supply chain by integrating a multi- agent approach using simulation

This study proposes an architecture based on multiple agents through a Colored Petri Net model (CPN), which enables the integration of supply chain links and information technology to speed up the production process and services. The methodology was developed based on three fundamental concepts: CPN as design tool, Supply Chain (SC) Architecture and Multi-Agent Systems (MAS). As a case study, a typical supply chain in the Colombian production system was considered. As a case study, a typical supply chain in the Colombian production system was considered. For this, the study developed a modeling procedure to transform its supply chain architecture from a typical structure to a multi-agent structure. This procedure consists of seven phases. Initially, the case study is characterized as a supply chain, then the information is collected, next the limitations of the supply chain are identified, then the variables and their interactions are defined. In the following two phases, three models are developed: the structural model, representing the physical distribution of the plant; the conceptual model, that relates the system components and their interactions; and the functional model, which integrates the previous models and associates them with the behavior of the variables during the process ow. Finally, a qualitative and quantitative validation of the models are performed, simulating different scenarios. The results show that a distributed supply chain structure, using multi-agent architecture, first, offers robust information and physical flows, and secondly, contributes to supply time reduction, communication quickness, efficiency and quality of information for operational decision making. The results could also motivate organizations to integrate the supply chains considering MASs in resource management.

Line Art Colorization Based on Explicit Region Segmentation

AbstractAutomatic line art colorization plays an important role in anime and comic industry. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce an explicit segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding artifacts. This mechanism is able to provide region segmentation information for the colorization process explicitly so that the colorization model can learn to avoid assigning the same color across regions with different semantics or inconsistent colors inside an individual region. The proposed mechanism is designed in a plug‐and‐play manner, so it can be applied to a diversity of line art colorization frameworks with various kinds of user guidances. We evaluate this mechanism in tag‐based and reference‐based line art colorization tasks by incorporating it into the state‐of‐the‐art models. Comparisons with these existing models corroborate the effectiveness of our method which largely alleviates the color bleeding artifacts. The code is available athttps://github.com/Ricardo-L-C/ColorizationWithRegion.

Railway track availability modelling with opportunistic maintenance practice

The geometry of railway track must be maintained within certain standards in order to provide a highly available network, as well as good ride comfort and safety for all rail users. Modelling is well utilised as part of asset management tools in exploring the effectiveness of different rail geometry maintenance strategies. By considering the rail route as an entire system – in contrast to a track section in isolation – a more effective maintenance strategy can be developed, including the deployment of opportunistic maintenance practice. This study presents a Coloured Petri Net model of railway track degradation, inspection, and maintenance planning and delivery, for an entire route of track sections. Opportunistic maintenance is introduced through a novel search transition function, which groups interventions based on local adjacency. Testing explores the availability and maintenance demands expected when following a series of different management strategies. This is extended to testing under heatwave conditions, a known disrupter to track geometry maintenance delivery. Simulation results show that in following an opportunistic strategy, greater availability can be achieved on the modelled rail line. Further, resilience to the heatwave disruptions can be achieved by selecting the correct maintenance strategy parameters. This asset management tool can provide guidance on management strategies for a full route of track sections as a combined system.

Colorization by Matrix Completion

Given a monochrome image and some manually labeled pixels, the colorization problem is a computer-assisted process of adding color to the monochrome image. This paper proposes a novel approach to the colorization problem by formulating it as a matrix completion problem. In particular, taking a monochrome image and parts of the color pixels (labels) as inputs, we develop a robust colorization model and resort to an augmented Lagrange multiplier algorithm for solving the model. Our approach is based on the fact that a matrix can be represented as a low-rank matrix plus a sparse matrix. Our approach is effective because it is able to handle the potential noises in the monochrome image and outliers in the labels. To improve the performance of our method, we further incorporate a so-called local-color-consistency idea into our method. Empirical results on real data sets are encouraging.

Semantic‐aware automatic image colorization via unpaired cycle‐consistent self‐supervised network

Automatic image colorization without manual interventions is an ill-conditioned and inherently ambiguous problem. Most of existing methods focus on formulating colorization as a regression problem and learn parametric mappings from grayscale to color through deep neural networks. Due to the multimodalities of color-grayscale space, in many applications, it is not required to recover exact ground-truth color. Pair-wise pixel-to-pixel learning-based algorithms lack rationality. Techniques such as color space conversion techniques are then proposed to avoid such direct pixel learning. However, the coloring results after color space conversion are blunt and unnatural. In this paper, we hold viewpoints that a reasonable solution is to generate some colorized result that looks natural. No matter what color a region is to be assigned, the colorized region should be semantically and spatially consistent. In this paper, we propose an effective semantic-aware automatic colorization model via unpaired cycle-consistent self-supervised network. Low-level monochrome loss, perceptual identity loss and high-level semantic-consistence loss, together with adversarial loss, are introduced to guide network self-training. We train and test our model on randomly selected subsets from PASCAL VOC 2012. The experimental results including human subjective studies demonstrate that, compared with state-of-the-art methods, our proposed model can achieve more convincing and superior results. Relevant source code is available at https://github.com/YuSuen/ACCycleGAN.

Colorization of 3D Objects Based on Geometric Generation Model

Colorization of gray image is a challenging research topic which is often discussed. It can be used to restore photos and assist artistic work. There are many approaches to colorize. From the beginning of the use of artificial colorization, and then the method of automatic colorization appeared. Most automatic colorization methods are based on deep learning. Most previous studies have focused on 2D image, the colorization of 3D objects is rarely discussed. In this paper, we introduce the colorization of 3D objects based on the geometric generation model. Unlike 2D images, 3D objects contain different information. Therefore, there are different problems in colorization. Colorization of 2D image can be regarded as the mapping of grayscale texture and color information. This mapping can be simulated by neural network, such as Variational Autoencoder and Generative Adversarial Networks. For 3D objects, we can use similar method for automatic colorization. In this paper, we introduce a generation model for colorization of 3D objects.

A factor model for detection and recognition of human face contours and elements

The paper deals with the issues of detection and modeling of human faces and objects on a face taken from the images. The model, algorithm and program are developed for the detection of human facial contours and main elements. The preliminary image processing involves the methods of color modeling and color measurements. Well-known methods, including hidden Markov models, are used for image recognition and processing. The training of the developed model was carried out with neural network methods of machine learning based on a specially created sample, as well as using color segmentation methods. A factor model of a human face is created, which makes it possible to select and recognize efficiently a face and its objects in the image at high speed and with a given accuracy. The experiments have shown that the accuracy of the correct selection of boundaries was about 95–96 % after training. The developed model can be used in security assurance tasks, namely to search and identify criminals, to strengthen law and order, to control access to critical infrastructure facilities, etc.

Analysis of the Current Situation of Piano Education in Colleges and Universities in the Information Age and Research on Countermeasures

In view of the low application ability of piano improvisational accompaniment of music majors, this paper proposes a method of big data combined with MIDI keyboard and Kinect depth sensor to achieve the purpose of recognizing chord progression and judging fingering when students perform, and realizes the auxiliary teaching system. Firstly, the information of color and depth images is obtained, and the state transition diagram of chord transposition and chord gesture template library are constructed as the system initialization conditions. Secondly, using the traditional skin color modeling and background difference method as well as the current depth data, the gesture recognition is realized by template matching. Finally, the correctness of chord progression is judged, and comprehensive fingering application is used to score and evaluate. The experimental results show that the system has high robustness and can be effectively applied to piano teaching.

Mathematical Modeling of Color Printing Devices: Color Gamut Visualization and Colorimetric Measurements Regularization

The article concerns a piecewise linear modeling of a vast range of color printing devices: various printers of different kind and nature, presses, etc. The central purpose is not a presentation of ready to use computational algorithms, or, moreover, accomplished software solutions. The point is to reveal some aspects of mathematical modeling, which could serve as both a guideline for creation of practical and robust engineer solutions and an example of nontrivial application of piecewise linear topology.

Fast and robust interactive image segmentation in bilateral space with reliable color modeling and higher order potential

We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random field energy on the vertices of the bilateral grid that can be solved efficiently using a standard graph cut label assignment. To achieve this, we introduce reliable color models to distinguish the foreground and background despite the presence of extremely difficult cases and a higher-order potential to encourage spatial consistency in segmentation. We conduct comprehensive experiments on three standard interactive segmentation datasets, MSRA 10K, IIS, and PASCAL VOC 2012 segmentation validation set. The results show that the proposed method achieves competitive performance compared with state-of-the-art methods while making the current system efficient in terms of speed.

Research on Colour Modelling and Detecting System Based on Computer Big Data

Through the statistics and analysis of the three colour components of dynamic background pixels in RGB space, the paper found that the difference between the three components fluctuates in a narrow area. Based on this fact, it proposes RGB colour based on computer big data Background modelling method of component statistics. This method fully considers the correlation of the three colour components of RGB, and the foreground detection process is faster. Experimental results show that compared with other colour invariant methods, this algorithm can more accurately reduce the computational complexity, time consumption and memory consumption of foreground detection.

Microstructures amplify carotenoid plumage signals in tanagers

Brilliantly-colored birds are a model system for research into evolution and sexual selection. Red, orange, and yellow carotenoid-colored plumages have been considered honest signals of condition; however, sex differences in feather pigments and microstructures are not well understood. Here, we show that microstructures, rather than carotenoid pigments, seem to be a major driver of male–female color differences in the social, sexually-dimorphic tanager genus Ramphocelus. We comprehensively quantified feather (i) color (using spectrophotometry), (ii) pigments (using liquid chromatography–mass spectrometry (LC–MS)), and (iii) microstructures (using scanning electron microscopy (SEM) and finite-difference time-domain (FDTD) optical modeling). Males have significantly more saturated color patches than females. However, our exploratory analysis of pigments suggested that males and females have concordant carotenoid pigment profiles across all species (MCMCglmm model, female:male ratio = 0.95). Male, but not female, feathers have elaborate microstructures which amplify color appearance. Oblong, expanded feather barbs in males enhance color saturation (for the same amount of pigment) by increasing the transmission of optical power through the feather. Dihedral barbules (vertically-angled, strap-shaped barbules) in males reduce total reflectance to generate “super black” and “velvet red” plumage. Melanin in females explains some, but not all, of the male–female plumage differences. Our results suggest that a widely cited index of honesty, carotenoid pigments, cannot fully explain male appearance. We propose that males are selected to evolve amplifiers—in this case, microstructures that enhance appearance—that are not necessarily themselves linked to quality.

Automatic Grayscale Image Colorization using a Deep Hybrid Model

Image colorization is an interesting yet challenging task due to the descriptive nature of getting a natural-looking color image from any grayscale image. To tackle this challenge and also have a fully automatic procedure, we propose a Convolutional Neural Network (CNN)-based model to benefit from the impressive ability of CNN in the image processing tasks. To this end, we propose a deep-based model for automatic grayscale image colorization. Harnessing from convolutional-based pre-trained models, we fuse three pre-trained models, VGG16, ResNet50, and Inception-v2, to improve the model performance. The average of three model outputs is used to obtain more rich features in the model. The fused features are fed to an encoder-decoder network to obtain a color image from a grayscale input image. We perform a step-by-step analysis of different pre-trained models and fusion methodologies to include a more accurate combination of these models in the proposed model. Results on LFW and ImageNet datasets confirm the effectiveness of our model compared to state-of-the-art alternatives in the field.

Color-UNet++: A resolution for colorization of grayscale images using improved UNet++

Colorization is the computer-assisted application of color to a gray scale image, which presents two problems to modern deep learning-based approaches. One is to provide colorization models with both high expressibility and strong learning ability, as current models have difficulty both excelling at coloring and being easy to train. The other is to return a picture without uneven overlap. This paper proposes a deep convolutional network framework called Color-UNet++ for the end-to-end solution of these colorization problems. Color-UNet++ is adjusted to settle gradient dispersion and explosion by capturing more transfer and intermediate results during backpropagation. We adjust the de-convolution structure to solve the problem of uneven overlap. We design the model in YUV instead of RGB color space, with an objective function that is appropriate to the coloring problem and can capture a wide range of colors. A large number of experimental results on LFW and LSUN datasets confirm the method’s superiority.

It’s Dust: Solving the Mysteries of the Intrinsic Scatter and Host-galaxy Dependence of Standardized Type Ia Supernova Brightnesses

Abstract The use of Type Ia supernovae (SNe Ia) as cosmological tools has motivated significant effort to understand what drives the intrinsic scatter of SN Ia distance modulus residuals after standardization, characterize the distribution of SN Ia colors, and explain why properties of the host galaxies of the SNe correlate with SN Ia distance modulus residuals. We use a compiled sample of ∼1450 spectroscopically confirmed photometric light curves of SNe Ia and propose a solution to these three problems simultaneously that also explains an empirical 11σ detection of the dependence of Hubble residual scatter on SN Ia color. We introduce a physical model of color where intrinsic SN Ia colors with a relatively weak correlation with luminosity are combined with extrinsic dust-like colors (E(B − V)) with a wide range of extinction parameter values (R V ). This model captures the observed trends of Hubble residual scatter and indicates that the dominant component of SN Ia intrinsic scatter is variation in R V . We also find that the recovered E(B − V) and R V distributions differ based on global host-galaxy stellar mass, and this explains the observed correlation (γ) between mass and Hubble residuals seen in past analyses, as well as an observed 4.5σ dependence of γ on SN Ia color. This finding removes any need to ascribe different intrinsic luminosities to different progenitor systems. Finally, we measure biases in the equation of state of dark energy (w) up to ∣Δw∣ = 0.04 by replacing previous models of SN color with our dust-based model; this bias is larger than any systematic uncertainty in previous SN Ia cosmological analyses.

Арт-техники в подготовке музыканта-исполнителя

The authors of the article consider one of the directions of musical pedagogy - the use of art pedagogy in the process of training a musician-performer. Based on the analysis of the research literature and experience describes the possibility of using psychological reserves of music and performing activities, in particular, reception of musical color modeling of the emotional and figurative content of music, are revealed.

Colour and chlorophyll level modelling in vacuum-precooled green beans during storage

Abstract To investigate the changes in colour and chlorophyll levels in vacuum-precooled green beans during storage, kinetic models and shelf life prediction models of these quality indices were established by reaction kinetics and the Arrhenius equation. Linear regression analysis was used to study the changes in colour and chlorophyll levels during 12 d of storage at 0 °C, 5 °C, 10 °C and 25 °C, and the relationship between chlorophyll level and total colour difference (ΔE) was also determined. The results showed that the colour and chlorophyll levels retained in vacuum-precooled green beans were better than those in the control group during storage. A linear regression analysis revealed that the change rate constants of colour parameters (L*, a*, b* and ΔE) and chlorophyll levels followed the Arrhenius law dependence on temperature. Colour and chlorophyll levels were successfully described by zero-order and first-order reactions, respectively. In addition, the shelf life based on ΔE and chlorophyll level changes of 10% was similar to that at a cut-off score of 6 by sensory evaluation. It was suggested that the kinetics model could accurately predict the shelf life and changes of colour and chlorophyll levels in vacuum-precooled green beans during storage.

Response Surface Methodology: Photocatalytic Degradation Kinetics of Basic Blue 41 Dye Using Activated Carbon with TiO2.

Water decontamination still remains a major challenge to some developing countries not having centralized wastewater systems. Therefore, this study presents the optimization of photocatalytic degradation of Basic Blue 41 dye in an aqueous medium by an activated carbon (AC)-TiO2 photocatalyst under UV irradiation. The mesoporous AC-TiO2 synthesized by a sonication method was characterized by X-ray diffraction (XRD) and Fourier-transform infrared (FTIR) spectroscopy for crystal phase identification and molecular bond structures, respectively. The efficiency of the AC-TiO2 was evaluated as a function of three input variables viz. catalyst load (2–4 g), reaction time (15–45 min) and pH (6–9) by using Box-Behnken design (BBD) adapted from response surface methodology. Using color and turbidity removal as responses, a 17 run experiment matrix was generated by the BBD to investigate the interaction effects of the three aforementioned input factors. From the results, a reduced quadratic model was generated, which showed good predictability of results agreeable to the experimental data. The analysis of variance (ANOVA), signposted the selected models for color and turbidity, was highly significant (p < 0.05) with coefficients of determination (R2) values of 0.972 and 0.988, respectively. The catalyst load was found as the most significant factor with a high antagonistic impact on the process, whereas the interactive effect of reaction time and pH affected the process positively. At optimal conditions of catalyst load (2.6 g), reaction time (45 min), and pH (6); the desirability of 96% was obtained by a numerical optimization approach representing turbidity removal of 93% and color of 96%.

Why Variation in Flower Color May Help Reproductive Success in the Endangered Australian Orchid Caladenia fulva

Caladenia fulva G.W. Carr (Tawny Spider-orchid) is a terrestrial Australian endangered orchid confined to contiguous reserves in open woodland in Victoria, Australia. Natural recruitment is poor and no confirmed pollinator has been observed in the last 30 years. Polymorphic variation in flower color complicates plans for artificial pollination, seed collection and ex situ propagation for augmentation or re-introduction. DNA sequencing showed that there was no distinction among color variants in the nuclear ribosomal internal transcribed spacer (ITS) region and the chloroplast trnT-trnF and matK regions. Also, authentic specimens of both C. fulva and Caladenia reticulata from the reserves clustered along with these variants, suggesting free interbreeding. Artificial cross-pollination in situ and assessment of seed viability further suggested that no fertility barriers existed among color variants. Natural fruit set was 15% of the population and was proportional to numbers of the different flower colors but varied with orchid patch within the population. Color modeling on spectral data suggested that a hymenopteran pollinator could discriminate visually among color variants. The similarity in fruiting success, however, suggests that flower color polymorphism may avoid pollinator habituation to specific non-rewarding flower colors. The retention of large brightly colored flowers suggests that C. fulva has maintained attractiveness to foraging insects rather than evolving to match a scarce unreliable hymenopteran sexual pollinator. These results suggest that C. fulva should be recognized as encompassing plants with these multiple flower colors, and artificial pollination should use all variants to conserve the biodiversity of the extant population.