Object Color Research Articles

Representation of the Green Color in Journalistic Texts by English and German Nouns and Verbs

The purpose of the proposed work is to identify similarities and differences in the manifestation of green symbolism in English and German journalistic texts, the sources of which were online media. The subject of the study is the semantics of English and German nouns and verbs representing the green color in the compared languages. The symbolism of this color designation was chosen due to its insufficient study in the framework of comparative linguistics. In this article, based on the material of lexicographic sources, the dictionary meanings of these lexemes are determined, which can display the symbolism of the studied color in journalistic texts in English and German. The scientific novelty of this study is that for the first time it establishes similarities and differences in the transmission of green symbolism through nouns and verbs on the material of modern English and German journalistic texts. It was found that in English texts, in comparison with German texts, a greater number of nouns denoting green are used. In the course of the study, the comparative method, the method of semantic analysis, the modeling method, the quantitative method and the method of component analysis were used. As a result of the conducted research, it was revealed that in English journalistic texts, the symbolism of the green color, represented by nouns and verbs, is used to neutralize the green color of objects of material reality. In addition, the green color is able to display the positive symbolism of the permission of the requested action, environmental protection. In German journalistic texts, the symbolism of the green color can be both positive and negative. Basically, there is a positive symbolism of green when it comes to environmental protection. The symbolism can also be negative when the political activity of the Green Party in Germany is sharply criticized.

View cells in the hippocampus and prefrontal cortex of macaques during virtual navigation.

Cells selectively activated by a particular view of an environment have been found in the primate hippocampus (HPC). Whether view cells are present in other brain areas, and how view selectivity interacts with other variables such as object features and place remain unclear. Here, we explore these issues by recording the responses of neurons in the HPC and the lateral prefrontal cortex (LPFC) of rhesus macaques performing a task in which they learn new context-object associations while navigating a virtual environment using a joystick. We measured neuronal responses at different locations in a virtual maze where animals freely directed gaze to different regions of the visual scenes. We show that specific views containing task relevant objects selectively activated a proportion of HPC units, and an even higher proportion of LPFC units. Place selectivity was scarce and generally dependent on view. Many view cells were not affected by changing the object color or the context cue, two task relevant features. However, a small proportion of view cells showed selectivity for these two features. Our results show that during navigation in a virtual environment with complex and dynamic visual stimuli, view cells are found in both the HPC and the LPFC. View cells may have developed as a multiarea specialization in diurnal primates to encode the complexities and layouts of the environment through gaze exploration which ultimately enables building cognitive maps of space that guide navigation.

Color obstacle detection algorithm using AR drone 2.0 in a controlled environment

This work presents an obstacle detection algorithm using quadrotor AR. Drone 2.0 in a controlled environment with programming in OpenCV, ROS (Robot Operating System), and Python. The algorithm is based on the color object feature to determine it as an obstacle. It is the first step for future work where the drone is allowed to perform autonomous flights and obstacle avoidance. The ROS development environment makes it possible to adjust both the drone's linear and angular speed. The OpenCV management library allows processing the image obtained in real-time thanks to the cv_bridge package. The obstacle detection scenario is simulated using ROS-Gazebo, and then the algorithm is implemented in a real controlled environment. The results obtained were good; an obstacle detection system was successfully implemented using open-source tools.

Evaluation of peroxyacetic acid, liquid buffered vinegar, and cultured dextrose fermentate as potential antimicrobial interventions for raw chicken livers

AbstractThis study aimed to evaluate the use of peroxyacetic acid (PAA), buffered vinegar (BV), and cultured dextrose fermentate (CDF) to reduce Salmonella on artificially inoculated raw chicken livers, one of the most consumed offal around the world. Samples were inoculated with a 5‐strain cocktail of poultry‐borne Salmonella to obtain 106 CFU/g and immersed for 90 s with agitation in one of the following treatments: distilled water (control), 450 ppm PAA, 2.0% (w/v) BV, or 1.5% (w/v) CDF, prior to storing at 4°C. Salmonella was enumerated on XLD agar and monitored for 14 days. Data were analyzed using analysis of covariance. After immersion, there was a significant Salmonella reduction (p < .05) with all treatments, including the control. PAA resulted in the greatest numerical reduction at 0.65 ± 0.12 log; however, there were no significant differences in the reductions among all other treatments (p > .05). After 14 days, higher numerical reductions were observed for PAA, but only when compared to CDF. Although similar reductions (p > .05) were noted after 14 days except for CDF, Salmonella counts were lowest in all timepoints when PAA was used. PAA and CDF inhibited the growth of aerobic bacteria until day 3 while BV inhibited the growth up to 7 days. Regarding objective color, chicken livers immersed in PAA became lighter, but the difference was not sustained over time. No differences were observed in redness or yellowness values across any treatments.

Obstacle detection and avoidance system performed with drone using ROS and Python

The AR Drone 2.0 obstacle detection and avoidance system using ROS (Robot Operating System) as a development environment, OpenCV as a library for image processing, and Python as a programming language are presented. The algorithm is based on the object's color feature to determine whether it is an obstacle to allowing autonomous flights to the drone if the drone's battery charge level and its distance from the ground station are considered. The obstacle detection scenario is simulated using ROS-Gazebo, and then the algorithm is tested in a real controlled environment. It shows the proper functioning of the system, that is, it manages to detect a red object correctly and avoid it by the control actions implemented in the algorithm.

Developing a Quality Evaluation System for Color Reproduction of Color 3D Printing Based on MATLAB Multi-Metrics.

Color 3D printing has been widely used in many fields such as cultural, medical, industrial, and food. The color reproduction accuracy of 3D printed products in these fields is becoming increasingly demanding, which requires more reproduction methods and practical tools. At present, most color 3D printing devices use one quantitative index, that is, color difference, to directly predict the color reproduction quality. However, this single quantitative index is not optimal for the curved surface of 3D printed color objects. Based on color evaluation principles, in this study, five new quantitative metrics consisting of color gamut comparison index, color SSIM index, color FSIM index, iCID index, and subjective scaling values are proposed for comparison, and the corresponding GUI design and code implementation of new color quality evaluation system are performed by MATLAB. Moreover, the comprehensive color assessment of color 3D printed products is confirmed by utilizing standard image acquisition and microscopic imaging methods that are not limited to printing materials and sampling locations. The operation of this system is validated to provide interactivity, simplicity and high efficiency. As a result, the system can provide new valuable feedback for color separation and output calibration of color 3D printing devices.

Water Level Detection for Flood Disaster Management Based on Real-time Color Object Detection

Currently, the water level monitoring system for a river uses instruments installed on the banks of the river and must be checked continuously and manually. This study proposes a real-time water level detection system based on a computer vision algorithm. In the proposed system, we use color object tracking technique with a bar indicator as a reference’s level. We set three bar indicators to determine the status of the water level, namely NORMAL, ALERT and DANGER. A camera was installed across the bar level indicators to capture bar indicator and monitoring the water level. In the simulation, the monitoring system was installed in 5-100 lux lighting conditions. For experimental purposes, we set various distances of the camera, which is set of 40-80 centimeters and the camera angle is set of 30-60 degrees. The experiment results showed that this system has an accuracy of 94% at camera distance is in range 50-80 centimeters and camera angle is 60o. Based on these results, it can be concluded that this proposed system can determine the water level well in varying lighting conditions.

Muscle Profiling of the Biceps Femoris, Gluteus Accessorius, and Gluteus Medius Comprising the Beef Top Sirloin Butt

Muscle profiling improves value and optimization of beef carcasses by expanding knowledge of physical, com-positional, and marketable attributes of single-muscle cuts. Extensive profiling for individual muscle portions of the NAMI #184 beef top sirloin butt remains understudied. The objective was to compare fluid loss, objective color (L*, a*, b*), pH,and objective tenderness of the biceps femoris (BF), gluteus accessorius (GA), gluteus medius, dorsal (GMD), and gluteus medius, ventral (GMV). Beef top sirloin butts (N=70) were collected from carcasses ranging in quality grade (USDA Select and Top Choice), hot carcass weight (light≤362 kg, medium=363 to 453 kg, heavy≥454 kg), and ribeye area (REA; small≤27.8 cm2, medium=27.9 to 40.6 cm2, large≥40.7 cm2). Warner-Bratzler shear force values were the low-est for the BF and GA (P<0.001) and were significantly different than the GMD and GMV. The GA reported the lowest percentage of fluid loss in raw and cooked forms (P<0.001, P<0.001) and the highest pH (P<0.001). The GMD exhibited the highest percentage of raw purge (P<0.001), highest L* value (P<0.001), and highest shear force (P<0.001). The GMV had the highest percentage of cook loss (P<0.001). USDA Top Choice muscles were more tender than Select (P<0.001) with higher L* value (P<0.001). All 4 top sirloin muscles and muscle subunits had average peak shear force values below 3.9 kg, and thus, all were within the threshold for USDA “very tender.” These muscle profiling data will aid in identifying new beef value cuts from the top sirloin butt and assess acceptability of sirloin cuts for further retail and foodservice merchandising opportunities.

Modeling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks.

We modeled discrimination thresholds for object colors under different lighting environments [J. Opt. Soc. Am. 35, B244 (2018)]. First, we built models based on chromatic statistics, testing 60 models in total. Second, we trained convolutional neural networks (CNNs), using 160,280 images labeled by either the ground-truth or human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.

Modeling surface color discrimination under different lighting environments using image chromatic statistics and convolutional neural networks.

We modeled discrimination thresholds for object colors under different lighting environments [J. Opt. Soc. Am. 35, B244 (2018)]. First, we built models based on chromatic statistics, testing 60 models in total. Second, we trained convolutional neural networks (CNNs), using 160,280 images labeled by either the ground-truth or human responses. No single chromatic statistics model was sufficient to describe human discrimination thresholds across conditions, while human-response-trained CNNs nearly perfectly predicted human thresholds. Guided by region-of-interest analysis of the network, we modified the chromatic statistics models to use only the lower regions of the objects, which substantially improved performance.

Optimized and Efficient Color Prediction Algorithms Using Mask R-CNN

Color cognizant capability has a significant impact in service robots for object detection based on color, traffic signal interpretation for autonomous vehicles, etc. Conventional clustering algorithms such as K-means and mean shift can be used for predicting the dominant color of an image by mapping the pixels from RGB to HSV and clustering them based on HSV values, thereby picking the cluster with the most pixels as the dominant color of the image, but these approaches are not solely dedicated to the same outcome. This research’s goal is to introduce novel techniques for predicting the dominant color of objects in images, as well as pixel extraction concepts, which allow these algorithms to be more time and efficiency optimized. This investigation appraises propriety of integrating object detection and color prediction algorithms. We introduce a dominant color prediction color map model and two new algorithms: average windowing and pixel skip. To predict objects in an image prior to color prediction, we combined the Mask R-CNN framework with our proposed techniques. Verification of our approach is done by creating a benchmark dataset of 200 images and comparing color predicted by algorithms with actual color. The accuracy and runtime of existing techniques are compared with those of the proposed algorithms to prove the superiority of our algorithms. The viability of the proposed algorithms was demonstrated by scores of 95.4% accuracy and color prediction time of 9.2 s for the PXS algorithm and corresponding values of 93.6% and 6.5 s for the AVW algorithm.

Smart Robot Vision for a Pick and Place Robotic System

The main contribution of this paper is to develop an innovative algorithm to accurately detect and identify the shape and color of objects under various light intensities and find their location to be manipulated by a pick-and-place robotic arm. Workpieces of various shapes and colors are dispersed on the robot's work plane and manipulated according to its specifications. The proposed algorithm utilizes the HSV color model to distinguish between different object colors and shapes. The S channel is used to detect the shapes of objects. After that, a series of filters (Median, Bilateral, and Gaussian) are applied to reduce the noise of the segmented image to make the process of discovering the shape and coordinates of the objects successful. The draw-contour method is used to discover the object’s shapes. After the shape of the object is discovered, the centroid coordinates are calculated. After extensive testing on 354 images that are captured in various lighting conditions in the range of (5-7000 lux), the overall system performance of 93.83% is achieved, and the average execution time is 2.21s. Finally, we had a dependable flexible automatic pick and place system that could correctly detect and identify the objects based on their features.

The Effectiveness of Trap Colors Against the Catch of Rice Plant Pests

The use of traps is one of the environmentally friendly pest controls. Insects have a preference for the colour of objects. The research aims to determine trap colours' effectiveness in catching rice plant pests. The research was conducted from June to July 2021 in Depok, Sleman Regency, Yogyakarta Special Region. The study used a descriptive observational method on an 8-week-old rice planting area. The colour traps used are white, red, green, yellow, and blue. The experiment used a completely randomized design with five replications. The results showed that the yellow trap produced the most insect catches, namely 545 insects, and the white colour produced the lowest catch of 74 heads. Pests that are heavily trapped in a row from the most to the least are Hydrelia sp, Leptocorisa oratorius, Nephotettix virescens, Pareaucosmetus sp, and Nilaparvata lugens. The most trapped is Hydrellia sp, as much as 78% of the total trapped insect pests. Effective trap installation time in the morning.

Inattentional blindness: Attentional set for efficient task success

Inattentional blindness is the failure to notice an unexpected object in plain sight when attention is otherwise engaged. We investigated what determines observers’ attentional set in a dynamic-counting inattentional blindness paradigm, when task instructions and visual distinctiveness of task-relevant objects were either congruent or in opposition. In seven experiments, observers counted bounces by task-relevant objects, with the instruction either to count-by-shape (squares, diamonds, crosses) or count-by-colour (blue, purple). To manipulate visual distinctiveness, we varied the extent to which task-relevant and task-irrelevant objects looked different on two dimensions: shape and colour. When colour better distinguished task-relevant from task-irrelevant objects, observers—even if instructed count-by-shape—reported an unexpected object that matched the colour of task-relevant objects. Crucially, when instructed count-by-colour, but shape better distinguished task-relevant from task-irrelevant objects, observers reported an unexpected object that matched the shape of task-relevant objects. We conclude that observers set their attention to promote efficient task performance.

TTQG (Topological Theory of Quantum Gravity) Driven New Theory of Color Physics

The concept of ‘color space’ in the conventional theories of ‘color physics’ is a concept of 3D color space. The magnitude of any color in the said theories is measured by mathematical integration of the tristimulus values of Red (X), Green(Y) and Blue (B) colors of light wave. However, the conventional theories of color physics are not able to predict the underlying cause of the existence of the 7 numbers of colors of the VIBGYOR or the Newton’s color wheel. Also the said theories cannot explain i) dimension of the light wave color and the object color, ii) the mechanism of evolution of the VIBGYOR colors from a typical ‘black body’ and iii) the ‘spectral power distribution(SPD) of Sun’s energy spectrum. In this research article through the ‘TOPOLOGICAL THEORY OF QUANTUM GRAVITY (TTQG)’, principally by considering the topologies and dimensions of mass (object), time, space expansion, space inversion, EM-wave, entopy, etc and others, all the above said mysteries have been revealed and presented here for the first time in the history of physics.

Binocular full-color holographic three-dimensional near eye display using a single SLM.

A binocular full-color holographic three-dimensional near eye display system using a single spatial light modulator (SLM) is proposed. In the display system, the frequency spectrum shifting operation and color spectrum shifting operation are adopted to realize the frequency division multiplexing (FDM) and frequency superposition multiplexing (FSM) by manipulating the frequency spectrums of each color- and view-channel sub-holograms. The FDM combined with polarization multiplexing will be used to implement binocular display using a single SLM, and the FSM working with a bandpass filter for each view-channel will be used to achieve full-color display from single frame hologram. The optical analysis and experiments with 3D color objects confirm the feasibility of the proposed system in the practical application.

A Method for Style Transfer from Artistic Images Based on Depth Extraction Generative Adversarial Network

Depth extraction generative adversarial network (DE-GAN) is designed for artistic work style transfer. Traditional style transfer models focus on extracting texture features and color features from style images through an autoencoding network by mixing texture features and color features using high-dimensional coding. In the aesthetics of artworks, the color, texture, shape, and spatial features of the artistic object together constitute the artistic style of the work. In this paper, we propose a multi-feature extractor to extract color features, texture features, depth features, and shape masks from style images with U-net, multi-factor extractor, fast Fourier transform, and MiDas depth estimation network. At the same time, a self-encoder structure is used as the content extraction network core to generate a network that shares style parameters with the feature extraction network and finally realizes the generation of artwork images in three-dimensional artistic styles. The experimental analysis shows that compared with other advanced methods, DE-GAN-generated images have higher subjective image quality, and the generated style pictures are more consistent with the aesthetic characteristics of real works of art. The quantitative data analysis shows that images generated using the DE-GAN method have better performance in terms of structural features, image distortion, image clarity, and texture details.

Analysis of the process of coloring objects based on the optical properties of objects

Color is a certain spectrum contained in a perfect light (white). The purpose of this study is 1). Can determine the cause of the color of the object (what causes a different object than the other color) 2). Can observe the color that occurs when two or more colors are mixed. The benefit in order to inform readers about what actually causes the color of the object and what if two or more colors are mixed. Mixing colors for color RGB, the colors Red to Green produce colors orange, blue color to produce a color Sian Green, Red color with blue produce magenta, and Red, Green with Blue produces white color. For CMY color mixing, the colors Cyan with yellow produce green color, magenta with yellow produces red color, the color Cyan by Magenta produces blue, and the colors Cyan, Magenta produces yellow Gray (Brown). Color objects can be invisible to the eye because of the reflection of the color of the object illuminated by light.

Evaluation of growth, meat quality, and sensory characteristics of wool, hair, and composite lambs.

The objectives of this study were to evaluate the growth rates, carcass quality, shelf-life, tenderness, sensory characteristics, volatile compounds, and fatty acid composition of wool, hair, and composite (wool × hair) lambs. Twenty-one wether lambs [wool (Suffolk × Polypay/Targhee; n = 7), hair (Dorper × Dorper; n = 7), and composite (Dorper × Polypay/Targhee; n = 7)] were fed from weaning to finishing at the University of Idaho Sheep Center and subsequently harvested under United States Department of Agriculture inspection at the University of Idaho Meat Lab. At 48 h postmortem, carcass measurements were taken to determine the percent boneless closely trimmed retail cuts, yield grade, and quality grade. Loins were fabricated from each carcass and wet-aged at 0°C until 10-d postmortem. Following aging, 2.54-cm bone-in loin chops were cut and randomly assigned to 4 d of retail display, Warner-Bratzler Shear Force (WBSF), or sensory analyses. Thiobarbituric acid reactive substances were analyzed on days 0 and 4 of retail display while subjective and objective color measurements were observed once daily. Samples (24 g) were also collected for volatile compound and fatty acid analysis. A mixed model analysis of variance was used to assess breed differences. Discernable effects were considered at P < 0.05. Wool lambs had heavier hot carcass weights (P < 0.001), larger rib-eye area (P = 0.015), and higher dressing percent (P < 0.001) than the other breeds. There was an interaction observed between breed and days of retail display for browning (P = 0.006). On day 1 chops from the composite breed had more browning than chops from the wool breed. No differences were observed between groups for lean muscle L* values (P = 0.432), a* values (P = 0.757), and b* values (P = 0.615). Differences were not observed in lipid oxidation (P = 0.159), WBSF (P = 0.540), or consumer acceptability (P = 0.295). There were differences found for 7 of the 45 fatty acids detected and in 3 of the 67 volatile compounds detected. In conclusion, wool lambs were heavier and had a greater carcass yield than the hair lamb carcasses. Regardless of breed, consumers did not detect sensory traits that would impact their eating experience.

Voice Guidance System for Color Recognition Based on IoT

We often need to identify the colors of such objects as buildings, clothing, photos, and traffic signs in everyday life. Some people cannot distinguish among such colors. This study proposes a method to help people, including colorblind people, identify colors. We establish a platform for color identification that is connected by the Internet of Things (IoT). The Espressif Systems 32 (ESP32) single chip is connected to a Wi-Fi communication network to transmit data to the color identification platform. The system interface is displayed in the form of color code and color name display. The speech synthesis module Speech Synthesizer Node 6288 (SYN6288) is used to broadcast color-related information. Since there are many color conversion technologies in modern times, or other color recognition methods are not applicable to the guide-blind function. Therefore, the authors use the method of reading out color-related information to directly enable colorblind people to conveniently identify the colors of objects. After experiments: the accuracy of various colors, the accuracy of environmental impact, the comparison of the original sensing experiment and the effect of adding interference light sources, the time required for color sensing to identify various colors, and the interference test results of any color light source, it is proved that the research mentioned in this research. The proposed method can not only improve the rate of recognition of the system in different environments, but can also accurately identify a variety of colors. The platform is integrated with the IoT to allow users to quickly monitor the displayed data.