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This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas. [ABSTRACT FROM AUTHOR]

Describing the behavior of an RGB backlight is complex but crucial in applications as local dimming with a variable backlight color. A color model to describe the interaction of the spectral backlight composition and the LC panel is the scope of this paper. The output of the model extends the commonly used one‐dimensional luminance to three‐dimensional tristimulus. Such a model requires that the physical characteristics of the RGB BLU as well as the LC‐panel are considered. The input variables include beyond the RGB subpixel gray values also the three duty ratios for the RGB‐LEDs. The calculation of the XYZ coordinates of an LC pixel, while an RGB backlight is varied, may get feasible. Also, a method for parameter extraction will be shown. [ABSTRACT FROM AUTHOR]

AbstractThis smartphone colorimetry investigated RGB (red, green and blue), HSV (hue, saturation and value of brightness) and CMYK (cyan, magenta, yellow and key) color parameters for characterizing bloodstains. Blood samples, separately deposited on cotton cloths and tissue papers, were analyzed by the ‘Colorimeter X’ application on an iPhone XR after 1, 5, 7, 9 and 11 days. The reductions in RGB and V values recorded by the smartphone application are consistent with darker bloodstain over time. Bloodstain colors are sensitive to the substrate and person-to-person variation, but a similar correlation with time could be established by a linear least squares fitting. With the highest coefficient of determination averaged from four samples, the V value is the most effective parameter for describing bloodstain ageing. For other color spaces, the R and K almost match the V value in measuring the bloodstains. Because these three values may be susceptible to the deviation in the measurement, the B value is suggested as an additional parameter in the bloodstain characterization. This small-scale study demonstrates the potential of colorimetric parameters in an on-site analysis by a cost-effective smartphone.

This smartphone colorimetry investigated RGB (red, green and blue), HSV (hue, saturation and value of brightness) and CMYK (cyan, magenta, yellow and key) color parameters for characterizing bloodstains. Blood samples, separately deposited on cotton cloths and tissue papers, were analyzed by the 'Colorimeter X' application on an iPhone XR after 1, 5, 7, 9 and 11 days. The reductions in RGB and V values recorded by the smartphone application are consistent with darker bloodstain over time. Bloodstain colors are sensitive to the substrate and person-to-person variation, but a similar correlation with time could be established by a linear least squares fitting. With the highest coefficient of determination averaged from four samples, the V value is the most effective parameter for describing bloodstain ageing. For other color spaces, the R and K almost match the V value in measuring the bloodstains. Because these three values may be susceptible to the deviation in the measurement, the B value is suggested as an additional parameter in the bloodstain characterization. This small-scale study demonstrates the potential of colorimetric parameters in an on-site analysis by a cost-effective smartphone. [ABSTRACT FROM AUTHOR]

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user’s reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user’s color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.

At the moment, there is a growing interest in composite materials with matrices based on thermoplastic polymers; these materials are superior to traditional carbon fiber plastics due to higher fracture toughness and impact strength, low smoke generation during combustion, and high thermal and chemical resistance. The deformation behavior of such composite materials due to the high plastic deformation of the matrix differs from the behavior of traditional composites, which must be considered when performing calculations. In our study, as a model object, the features of the microstructure of single carbon thread impregnated with polysulfone were studied in order to evaluate the distribution of misorientation angles of elementary fibers, the degree of their damage, the thickness of the polymer matrix interlayers between elementary fibers in threads impregnated with more viscous thermoplastic binders. Statistical analysis of a large array of micrographs was carried out using specially developed algorithms for computer processing of electron microscopic images of a composite material. Algorithms for processing arrays of images using machine vision algorithms are proposed. Based on the analysis of the data array, the distributions of carbon fibers impregnated with polysulfone were plotted over the misorientation angle of the filaments and over the interfilament distance in the longitudinal and cross sections of the fiber. Using the machine learning method, an algorithm for detecting violations of the filament structure was implemented. The data obtained can be used to refine the calculations of the strength and deformation characteristics of composite materials with thermoplastic matrices. [ABSTRACT FROM AUTHOR]

The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops' yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions. [ABSTRACT FROM AUTHOR]

The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions.

In this paper different fire detection systems and techniques has been reviewed, many techniques have been developed for the purpose of early fire detection in different scenarios. The most accurate technique used among all these methods is Image Processing based Techniques. Different color models like RGB, HSI, CIE L*a*b and YCbCr have been used along with different edge detection algorithms like Sobel and Novel edge detection, finally the color segmentation technique was discussed in the review paper. All the mentioned methods in these papers have significantly proved to detect fire and flame edges in digital images with a timely manner, which has a huge impact on saving life and reducing loss of life.

Algorithms for the analysis of polychromatic interference patterns in images of thin stratified objects in optical microscopy are considered. The algorithms allow one to measure the thin-film optical thickness. A measurement method based on the comparison of colors of the interference image under study and a numerically simulated image is discussed. We discuss a mathematical model for the calculation and numerical simulation of interference patterns and algorithms for interference pattern processing. Color comparison in an RGB color model is described and limitations of such a method are shown. The feasibility of using a Lab color model is shown and algorithms of interference color comparison in this model are presented. Results of application of the presented algorithms to measuring the optical thickness of red blood cells in a blood smear are discussed. The estimation of the error and robustness of the proposed algorithms is conducted.

Featured Application: The fundamental research is relevant to several applications involving dynamic conditions in terms of of incoming image's lightning and overall visibility of content present in it. Some of the applications include surveillance, student authentication in online learning environments, autonomous robotics. Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images' content visibility and thereby classify images. One of the key algorithms for calculation of images' content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image's pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms. [ABSTRACT FROM AUTHOR]

Shadows, in aerial and satellite high-resolution images of earth, are a common phenomenon. Shadow causes false-color image, loss of information in the image, and false image segmentation. This leads to incorrect outputs of many image processing applications. In this paper, we address the problem of shadow detection in aerial high-resolution images of earth. The paper presents a proposed method that can be valuable in comparing it with other existing methods. The proposed work exploits the impact of ratio image pixel values on the process of shadow detection. The ratio image is the mathematical division of hue over the intensity component in the invariant color model. We propose a method, design, and develop an algorithm. In the designed algorithm, the input RGB aerial image of the earth is transformed into the invariant color model hue, saturation, and value (HSV). It acquires the average intensity value of pixels of the input RGB image components. Then, the ratio image of Hue (H) over Value (V) is calculated. Afterward, a power function is applied to this ratio to modify it by increasing the difference between pixel values very effectively. Finally, a threshold is applied to the modified ratio image to classify pixels into shadow and nonshadow. The proposed power function helps the threshold to better classify pixels into shadow and nonshadow. It has been implemented and experimented extensively. A comparative study has also been made with existing methods in the literature. In comparing the proposed algorithm and some existing algorithms, the experimental results show that the proposed has the ability to detect shadows with satisfying accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a system is proposed for extracting insulators on power transmission lines from photographs captured by an unmanned aerial vehicle. The approximate regions of the insulators are first determined by a support vector machine with the histogram of oriented gradients as the feature descriptor. Then, the specific regions of insulators are detected and extracted by the GrabCut algorithm. In advance, some constraint conditions, such as the value ranges of color component values as well as the relationships between the component values in three color models, need to be specified. In our system, an interactive interface is developed to help determine these conditions. According to the experimental results, our system is capable of removing most of the backgrounds and extracting the insulators from photographs. [ABSTRACT FROM AUTHOR]

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

We are exposed to various aesthetic experiences everyday. Important variables involved in aesthetics are preference and harmony. Preference for color schemes can be predicted by combining preferences for the basic colors and ratings of color harmony. We use FHSI color model, in which color channels distributions are expressed with fuzzy membership functions. Such phenomena, as preference and harmony, are usually approximate and thus categorizing and ordering them is a challenge. Using fuzzy linguistic variables and hedges, we offer a human-consistent classification of aesthetic judgements. The main output of the model is aesthetic judgement on a harmony and personal preference level, e.g. very high preference. For example, in the context of apparel coordination, it allows predicting a preference for a look based on clothing colors. According to experiments, the model results in useful predictions of ratings of aesthetic pleasantness. Aesthetic preference prediction has potential in a wide range of domains, including e-commerce, design, marketing, and art. [ABSTRACT FROM AUTHOR]

In this paper, a twin support vector machine (TWSVM) based on improved artificial fish swarm algorithm (IAFSA) for fire flame recognition is proposed in view of the large computation burden and slow classification speed of the traditional support vector machine (SVM). Twin support vector machine is a machine learning algorithm developing from standard support vector machine. However, twin support vector machine cannot deal with the parameter selection problem well. The difficulty of parameter selection may greatly restrict the application of TWSVM in flame recognition problem. So a novel artificial fish swarm algorithm (AFSA) is used to solve the parameter selection problem of TWSVM. In order to make up for the drawbacks of the basic AFSA, the chaotic transformation is first applied to initialize the position of artificial fish swarm since it may be non-uniformly initialized in the basic artificial fish swarm algorithm. Then, the Cauchy mutation is used to make the fish swarm jump out of the local optimal solution after continuously expanding the visual scope of the artificial fish during the foraging procedure. An adaptively step-size adjusting method is then developed to optimize the moving steps of the swarming and following behaviors in order to accelerate the convergence speed of the developed algorithm. Last, to further improve the efficiency and accuracy of the algorithm, an elimination and regeneration mechanism based on adaptive t-distribution mutation is utilized to update the artificial fish swarm at each iterative procedure. Experimental results show that the TWSVM algorithm based on improved artificial fish swarm algorithm is a more effective method to identify the flame and greatly improves the accuracy and real-time performance of the flame recognition compared with PSO-TWSVM, Grid-TWSVM, GA-TWSVM, FOA-TWSVM, GSO-TWSVM, AFSA-TWSVM and the traditional SVM. [ABSTRACT FROM AUTHOR]

One of the most significant current discussions in Computer Science is the skin recognition. Many papers have studied the skin detection using different techniques, artificial vision, machine learning, deep learning among others. Despite its long success, the skin recognition has several problems in use. However, there has been little discussion about generate a skin tonalities classification. The aim of this paper is to propose a system to skin tonalities through geographic distribution based on clustering algorithms, pattern recognition and fuzzy logic. This distribution gives us the opportunity to classify the skin tonalities. We can study each skin tonality for any applications as medical diagnosis, security. In the first stage, we use the RGB color model to training the system. Then, we tested the system with different color models. We use color model with the best result to propose geographic distribution based skin tonalities. The results show that is possible to generate a skin tonalities classification. The proposed system is using to skin recognition, showing interesting results under controlled and no controlled conditions. [ABSTRACT FROM AUTHOR]

Image classification of a visual scene based on visibility is significant due to the rise in readily available automated solutions. Currently, there are only two known spectrums of image visibility i.e., dark, and bright. However, normal environments include semi-dark scenarios. Hence, visual extremes that will lead to the accurate extraction of image features should be duly discarded. Fundamentally speaking there are two broad methods to perform visual scene-based image classification, i.e., machine learning (ML) methods and computer vision methods. In ML, the issues of insufficient data, sophisticated hardware and inadequate image classifier training time remain significant problems to be handled. These techniques fail to classify the visual scene-based images with high accuracy. The other alternative is computer vision (CV) methods, which also have major issues. CV methods do provide some basic procedures which may assist in such classification but, to the best of our knowledge, no CV algorithm exists to perform such classification, i.e., these do not account for semi-dark images in the first place. Moreover, these methods do not provide a well-defined protocol to calculate images’ content visibility and thereby classify images. One of the key algorithms for calculation of images’ content visibility is backed by the HSL (hue, saturation, lightness) color model. The HSL color model allows the visibility calculation of a scene by calculating the lightness/luminance of a single pixel. Recognizing the high potential of the HSL color model, we propose a novel framework relying on the simple approach of the statistical manipulation of an entire image’s pixel intensities, represented by HSL color model. The proposed algorithm, namely, Relative Perceived Luminance Classification (RPLC) uses the HSL (hue, saturation, lightness) color model to correctly identify the luminosity values of the entire image. Our findings prove that the proposed method yields high classification accuracy (over 78%) with a small error rate. We show that the computational complexity of RPLC is much less than that of the state-of-the-art ML algorithms.

Fatigued driving detection, which employs pattern recognition to discover the state of a driver's fatigue, is considered a key technique to improve road safety. However, the widely used frontal face recognition systems have problems, such as low recognition accuracy, poor real-time ability, and highly complex algorithms. In this paper, a new color space modeling, which uses multi-threshold decision criteria, is used to enhance facial skin extraction performance, and a three-step strategy is designed to eliminate noise and other adverse effects. Experiments show that the proposed algorithm can extract side face contour lines effectively and provide a scientific basis for real-time tracking of fatigue.

There is increasing interest in flies as potentially important pollinators. Flies are known to have a complex visual system, including 4 spectral classes of photoreceptors that contribute to the perception of color. Our current understanding of how color signals are perceived by flies is based on data for the blowfly Lucilia sp. which after being conditioned to rewarded monochromatic light stimuli, showed evidence of a categorical color visual system. The resulting opponent fly color space has 4 distinct categories, and has been used to interpret how some fly pollinators may perceive flower colors. However, formal proof that flower flies (Syrphidae) only use a simple, categorical color process remains outstanding. In free-flying experiments, we tested the hoverfly Eristalis tenax , a Batesian mimic of the honeybee, that receives its nutrition by visiting flowers. Using a range of broadband similar–dissimilar color stimuli previously used to test color perception in pollinating hymenopteran species, we evaluated if there are steep changes in behavioral choices with continuously increasing color differences as might be expected by categorical color processing. Our data revealed that color choices by the hoverfly are mediated by a continuous monotonic function. Thus, these flies did not use a categorical processing, but showed evidence of a color discrimination function similar to that observed in several bee species. We therefore empirically provide data for the minimum color distance that can be discriminated by hoverflies in fly color space, enabling an improved understanding of plant–pollinator interactions with a non-model insect species. [ABSTRACT FROM AUTHOR]