Color Identification Research Articles

Do Task Sets Compete in the Stroop Task and Other Selective Attention Paradigms?

Task sets have been argued to play an important role in cognition, giving rise to the notions of needing to switch between active task sets and to control competing task sets in selective attention tasks. For example, it has been argued that Stroop interference results from two categories of conflict: informational and task (set) conflict. Informational conflict arises from processing the word and is resolved by a late selection mechanism; task conflict arises when two task sets (i.e., word reading and colour identification) compete for activation and can be construed as involving an early selection mechanism. However, recent work has argued that task set control might not be needed to explain all of the switching cost in task switching studies. Here we consider whether task conflict plays a role in selective attention tasks. In particular, we consider whether S-R associations, which lead to informational conflict, are enough on their own to explain findings attributed to task set conflict. We review and critically evaluate both the findings that provided the original impetus for proposing task conflict in selective attention tasks and more recent findings reporting negative facilitation (longer RTs to congruent than to neutral stimuli) - a unique marker of task conflict. We then provide a tentative alternative account of negative facilitation based on poor control over informational conflict and apply it to a number of paradigms including the Colour-Object interference and Affordances tasks. It is argued that invoking competition between task sets in selective attention tasks might not be necessary.

Feature ghost imaging for color identification.

On the basis of computational ghost imaging (CGI), we present a new imaging technique, feature ghost imaging (FGI), which can convert the color information into distinguishable edge features in retrieved grayscale images. With the edge features extracted by different order operators, FGI can obtain the shape and the color information of objects simultaneously in a single-round detection using one single-pixel detector. The feature distinction of rainbow colors is presented in numerical simulations and the verification of FGI's practical performance is conducted in experiments. Furnishing a new perspective to the imaging of colored objects, our FGI extends the function and the application fields of traditional CGI while sustaining the simplicity of the experimental setup.

Constructing a Forest Color Palette and the Effects of the Color Patch Index on Human Eye Recognition Accuracy

As the first visual element, color is the most attractive in the forest landscape. There are various kinds of forest colors; however, the human eye’s ability to recognize them is limited. In order to combine color composition and human eye recognition ability to quantify forest colors more appropriately and to improve the ornamental effect of forest color landscapes more precisely, we have constructed a forest color palette using k-means clustering based on the color information of 986 forest images from 40 national forest parks in China. The differences in color recognition accuracy and sensitivity among populations and colors were analyzed. The effect of forest color patch indices on color identification accuracy for interior and distant forest landscapes was also explored. The results were as follows: (1) forest color could be divided into eight color families—orange, yellow, yellow-green, green, blue-green, blue, purple, and red. (2) For humans, the recognition accuracy was highest for green and lowest for blue-green. (3) For interior forest landscapes, the mean area proportion and fractal dimension of the color patches showed significant positive effects on color recognition accuracy, whereas the number and density of color patches showed significant negative effects. For distant forest landscapes, the density and Shannon’s diversity index of the color patches showed significant positive effects for color recognition accuracy, whereas the number, edge density, division index, and cohesion of the color patches showed significant negative effects. We thus suggest that it is necessary to increase the complexity of the color patch shape when creating interior forest landscapes and to focus on the diversity and balance of color matching when creating distant forest landscapes. In future studies, the collection pathways for forest images should be expanded, and color information extraction algorithms that incorporate human perception should be selected. This will improve the data available for forest color studies and enable the construction of a more accurate forest color palette.

Visual modelling can optimise the appearance and capture efficiency of sticky traps used to manage insect pests

Sticky traps are one of the most important tools for monitoring and mass trapping of insect pests. Their effectiveness depends on attracting and capturing target pests efficiently. Trap colour strongly affects capture rates, but currently a principled approach to identifying optimal trap colour for a given pest and growing context is lacking. Here we propose that modelling pest colour vision enables identification of trap colours that optimise pest capture rates. We test this novel approach to trap design in field trials on Western flower thrips (WFT) Frankiniella occidentalis, an economically damaging pest of agriculture and horticulture worldwide. Prior studies have reported that WFT prefer blue and yellow sticky traps, aligning with recent evidence that WFT have trichromatic colour vision with peak sensitivities in the UV, blue and green portion of the visual spectrum and a blue-green colour opponent mechanism. Therefore, we hypothesised that a shade of blue that maximally stimulates the blue photoreceptor whilst minimally stimulating the green photoreceptor would improve sticky trap capture rates, while a shade of blue that decreased the opponent response would reduce capture rates. In three field experiments, we found strong support for this hypothesis: the optimised blue colour captured 1.3–2.6 times more WFT than current commercial trap colours. Our results also demonstrated that visual modelling can identify optimally contrasting colours for two-colour traps that further improve capture rates. This study provides a novel and principled approach to the design of visual traps that could be extended to other pest management contexts.

Elucidation of the effect of heat exposure on hair colored by permanent and semipermanent colorants using surface‐enhanced Raman spectroscopy

Confirmatory identification of hair colorants can be used to establish a connection between a suspect and the crime science or demonstrate the absence of such connections. A growing body of evidence shows that surface-enhanced Raman spectroscopy (SERS) could be a confirmatory, minimally destructive, and fully noninvasive analysis of hair colorants. In SERS, a signal that provide the information about the chemical structure of both permanent and semipermanent dyes present on hair is enhanced by a million-fold using noble metal nanostructures. However, it is unclear whether the information of hair colorants can be revealed if hair was contaminated or exposed to harsh environments such as sunlight and heat. In this work, we determine the effect of a short- and long-term heat exposure on SERS-based analysis of hair colored with blue and red permanent and semipermanent dyes. We found that short and especially long-term heat exposure at 220°C could alter chemical structure, and consequently SERS spectra, of permanent and semipermanent colorants. This thermal degradation of permanent dyes complicates their direct identification using SERS. We also found that partial least squares discriminant analysis can be used to overcome this issue allowing for highly accurate identification of both permanent and semipermanent dyes on colored hair that was exposed to 220°C for 6-12 min. These results show that heat exposure of colored hair should be strongly considered upon their SERS-based examination to avoid both false positive or false negative identification of chemical dyes.

Wool dyeing using Ziziphus bark extract as a natural dye: studies on the dyeing, antibacterial, and antioxidant characteristics.

Considering the growing importance of natural colorants and sustainable products, the research on application of natural dyes has been focused on new color resources, identification, and standardization of natural dyes. Hence, the extraction of natural colorants available in Ziziphus bark was performed by ultrasound technique and its application on the wool yarn to produce the antioxidant and antibacterial fibers. The optimal conditions for the extraction process were as follows: ethanol/water (1/2 v/v) as solvent, concentration of Ziziphus dye 14g/L, pH 9, temperature 50°C, time 30min, and L.R ratio 50:1. Moreover, the effect of important variables for application of Ziziphus dye on the wool yarn was investigated and optimized temperature 100°C, concentration of Ziziphus dye 50% o.w.f., time for dyeing 60min and pH 8, and L.R 30:1. The reduction values of Gram-negative and Gram-positive bacteria on dyed samples at optimized condition were 85% and 76%, respectively. Moreover, the antioxidant property of dyed sample was 78%. The color variations on the wool yarn were produced with diverse metal mordants, and color fastness properties were measured. Ziziphus dye not only can be used as an origin for a natural dye, but also provided the antibacterial and antioxidant agent on the wool yarn, which can be a step towards the fabrication of green products.

Forensic Phenotyping

Forensic DNA phenotyping [FDP] is a new approach that uses DNA to create probabilistic inferences about a person’s observable traits. It is an amazing fact that helps us to learn about something from the object that they left behind at the scene of crime. The most discarded items like cigarette buds, chewing gums, Tobacco residue and bidi. From this discarded waste, it helps to extract a trace amount of DNA evidence to create a DNA profile of a person which helps to reconstruct a person’s face. This process can be done, with the identification of hair colour, height, eye colour, sex, age, and even morphology of the face, and the ancestry of criminal and accused persons with the help of their genetic constituents.

Preparation and Evaluation of Herbal Toothpaste

The majority of people use toothpaste regularly.Typically, tootpaste is used to clean the mouth and teeth. It is also used to treat a variety of dental conditions. A lot of dentists advise using toothpaste to cure conditions including sensitivity and chronic gingivitis, among others. Herbal extracts of numerous crude medications with antibacterial and antimicrobial properties can be used to make herbal toothpaste. Herbs like ginger, indica extracts, babul leaves extract, ginger extract, lemon oil extract, neem stem & bark, babul leaves, gauva leaves, kalmi bark, etc. are used in the preparation of herbal toothpaste formulations. [1] Preparing, assessing, and contrasting lab-made herbal toothpaste with commercial herbal toothpastes is the major goal of the current effort. Commercial herbal toothpastes including Himalaya, Meswak, and Dent County have all had their quality assessed in the current study. All of the examined commercially available herbal tooth pastes and lab-made herbal toothpaste met the requirements outlined by the Bureau of Indian Standards. [2] The goal of the ongoing research is to create herbal toothpaste using chemicals like camphor, honey, and plant extracts including neem, tulshi leaves, and guava leaves. In formulating herbal toothpaste, characteristics like colour, spreadability, foamability, and particle identification of shard and edge abrasives were assessed. [3] Neem, Clove, Betel, Peppermint, Turmeric, and Guava are six plant samples included in this experiment that are typically used as traditional medicines. The created toothpastes were assessed in accordance with Bureau of Indian Standards requirements. The effectiveness of the tested toothpastes’ antibacterial properties was found. [4] One of the most important needs for humans is oral hygiene. Oral hygiene is the first step in a human being’s daily life. Therefore, toothpaste is crucial to this process. To counteract some drawbacks of synthetic cleaning agents, many natural herbs might be used. [5].

The effects of sun exposure on colorant identification of permanently and semi-permanently dyed hair

During bloating and active decay, human remains begin to deform and warp their physical identity. After the skin and muscles loosen and detach from their skeletal structuration, everything but bones, teeth, and hair will fully disintegrate into the soil that surrounds the body. Nearly half of people in the world dye their hair with a variety of permanent and semi-permanent colorants. Expanding upon this, we hypothesized that confirmatory analysis of hair colorants can be used to facilitate and advance forensic analysis of human remains. A growing body of evidence suggests that hair colorants can be identified directly on hair using surface-enhanced Raman spectroscopy (SERS). In this study, we investigate the extent to which SERS can be used to detect black and blue permanent and semi-permanent dyes on hair exposed to sunlight. Our results showed that although substantial photodegradation of all dyes was observed by week 7, SERS enabled highly accurate detection and identification of hair colorants during all 10 weeks of hair exposure to the sunlight with on average 99.2% accuracy. We also found that SERS could be used to predict fading rates of hair colorants. This information can shed light on the exposure of human remains to the exterior environment.

Identification of Color and Texture of Ripe Passion Fruit with Perceptron Neural Network Method

Research using artificial neural network methods has been developed as a tool that can help human tasks, one of which is for passion fruit UMKM entrepreneurs. The problem so far that has been faced by UMKM entrepreneurs of passion fruit is that it is difficult to identify ripe passion fruit with sweet and sour taste, because there are 6 colors of passion fruit and the color of passion fruit skin is visually slightly different, as well as the texture of maturity. The main purpose of this study was to identify the color structure and texture of the ripeness of passion fruit, in order to recognize the color and texture of the ripeness of passion fruit which is good for processing into syrup, jam, jelly, juice, passion fruit juice powder by entrepreneurs of UMKM of passion fruit. This study empirically tested the color and texture of the ripeness of 10 passion fruit using the perceptron artificial neural network learning method. The data is obtained from an image that will be entered into the program. The results of the identification process using the perceptron artificial neural network from the tests that have been carried out previously, the highest calculation results obtained with the best results using a learning rate of 0.8 and 500 epoch iterations and producing an accuracy of 80%.

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.

PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging

PLS-DA model for accurate identification of Chinese cabbage leaf color based on multispectral imaging

Surface-enhanced Raman spectroscopy hair analysis after household contamination.

Trace evidence found at crime scenes is rarely in an unsullied condition. Surface-enhanced Raman spectroscopy (SERS) is a modern analytical technique that can be used for the detection of artificial hair colourants (S. Higgins and D. Kurouski, Surface-Enhanced Raman Spectroscopy Enables Highly Accurate Identification of Different Brands, Types and Colors of Hair Dyes, Talanta, 2022, 251, 123762). However, contaminants pose a problem to collecting accurate spectra from the dyes. In this study, we sought to analyze how the different physical properties of contaminants can influence the collected spectra. We utilized 11 household substances of varying viscosity and opacity to contaminate hair dyed with permanent black or semi-permanent blue dyes. We discovered that contaminant opacity generally does not affect the spectral quality but that high contaminant viscosity does and that acidic substances could destroy the colourant's spectral identity altogether. Cleaning the contaminated hair with a water rinse allowed the underlying colourant to be identified in 21 out of 22 cases. Overall, this study provided a clearer understanding of the capabilities and limitations of SERS in forensic hair analysis.

Identification of Natural and Artificial Colorants in Industrial Products Marketed in Senegal

Identification of Natural and Artificial Colorants in Industrial Products Marketed in Senegal

Design of Boosted Cascade and RTLBP Features based Face Recognition Module Using ML in OpenCV

Over the past ten years, face recognition in images has become more and more important. Since facial recognition is a natural talent for humans, it could be challenging to train a computer to recognize faces. The detecting difficulty comprises three stages. Without the requirement for picture difference or skin color identification, the detector operates in real-time at fifteen frames per second. Proposed and developed is a face recognition system based on Real Time Local Binary Pattern (RTLBP) and Boosted Cascade of Simple Features (BCSF). Preprocessing, face identification and recognition, and annotation are the three stages of the system. Segmentation, filtering, thresholding, and improving image quality are all part of preprocessing. The face, which is crucial for identification, is identified using the BCSF approach. Every face in the database has a correlation and RTLBP calculation. Correlation and RTLBP analysis between the captured face and the database face is then used to conduct recognition. One statistical technique that falls within the large category of factor analysis is the Boosted Cascade of Simple Features. By reducing the massive quantity of data stored to the extent of the feature space needed to represent the data efficiently, the BCSF seeks to achieve this goal. The RTLBP is designed to recognize faces by using a wide 1-D pixel vector consisting of a 2-D face picture in compact main parts of the space function. This is referred to as self-space projection. The identity of the vectors in the covariance matrix, which is centred on a set of fingerprint images, helps determine the proper space. Using OpenCV, Open Computer Vision (OpenCV), Python, Eigen-face, Fisher Face, LBPH, and Haar Cascade programming, an algorithm for a camera-based, real-time face recognition system is developed in this project. The name of the detected individual appears in the top left corner of a highlighted rectangle that annotates the face. The process of recognition takes 10.18 seconds, with a 99.93% success rate.

Designing Digital Color Library as Inventory Media for The Natural Dye of Indonesian Traditional Textile (Study Case: Classical Batik of Yogyakarta)

Textile coloring using natural colorant has its own unique value on each color results as well as in the color-making creation process itself. In addition to the decorative elements, Indonesian traditional textile also has distinct signature textile culture on its natural dye, for example Batik Klasik Yogyakarta which is well known for its brown soga color, a brownish color obtained from the colorant’s combination of jambal bark (Pelthophorum ferrugineum), tingi bark (Ceriops candolleana), and tegeran wood (Cudrania javanensis). Along with those ingredients, Batik Klasik Yogyakarta also uses indigo plant (Indigofera tinctoria) as its blue colorant and kapur tohor (Calcium Oxide) for pre and post mordant treatment. The designing of “Manékawarna” digital content output as content of mobile application would be a recommendation as it would be a data inventory media of Batik Klasik Yogyakarta natural dye. In order to make the color visual data suitable for digital device, the research requires fabrics dyed with Batik Klasik Yogyakarta natural dye ingredients and technique as samples for color identification study by digitalization method to obtain RGB color value. The natural dye color digital format data is designed to facilitate data preservation and its acquisition by individuals whom have interest on textile natural dye.

“Gemo”: An AI-Powered Approach to Color, Clarity, Cut Prediction, and Valuation for Gemstones

“Gemo” is an AI-powered smartphone application that aims to improve the gem industry by replacing human-based approaches with computer-based ones. A mix of well-trained machine learning models that are capable of color identification, cut projection, recommendation, and pricing prediction is competent in offering experience and information to the industry. Traditional gem industry predictions are often subjective and inaccurate due to reliance on human labor. Erroneous output caused financial loss. Gemo is developed to overcome these problems by applying Artificial intelligence-based feature identification of gemstones and leads to expect more accurate and real-time results. Integration of Machine learning, Artificial Intelligence, and Natural Language Processing based technology, boosted the realism of the gem analysis process. “Gemo” is applied via advanced machine learning-based algorithms that capture the features of color, clarity shape, and intrinsic attributes of gemstones. Color detection model extraction of the Hue, Saturation, and Value (HSV) colors from gemstone pictures, delivering a cutting-edge and accurate approach to color recognition. The cut prediction approach lowers subjectivity and inaccuracy during the prediction of the cut by employing 3D image processing methods. The recommendation model gathers human preferences and forecasts the optimum solution using Natural Language Processing (NLP). Lastly, the valuation model utilizes the 4Cs features to provide a pricing range for the gemstones, resulting in a complete and advanced gemstone analysis system. The "Gemo" model, which integrates multiple ML-based models, increases the criteria for the gemstone sector. This will help gem experts excel and increase industry competitiveness.

At‐Home Data Collection with an App‐Based Cognitive Assessment for Detecting Memory and Executive Functioning Changes in Aging and Pre‐Clinical AD

Mobile, valid and engaging cognitive assessments are essential for detecting and tracking change in research participants and patients at risk for Alzheimer's Disease and Related Dementias (ADRDs). This pilot study aims to determine the feasibility of at-home, app-based memory and executive functioning tasks included in the mobile cognitive app performance platform (mCAPP), to detect cognitive changes associated with aging and preclinical AD. The mCAPP includes three gamified tasks (Figure 1): (1) a "concentration" memory task that includes learning and matching hidden card pairs with increasing memory load, pattern separation features (lure vs. non-lure), and spatial memory (2) a stroop-like task ("brick drop") with speeded word and color identification and response inhibition components and (3) a digit-symbol coding-like task ("space imposters") with increasing pairs and incidental learning components. Participants also completed the NACC UDS3, additional paper and pencil tests, and NIH Toolbox measures. Participants used the mCAPP at home for two weeks. Participants included sixteen older adults (56% female; age=71.4±5.0, education=16.4±3.1; 63% White, 31% Black, 6% Multiracial) without cognitive impairment enrolled in the Penn ADRC cohort. Participants played 12.1±4 times over two weeks, with 15/16 playing more than the assigned sessions. Almost all participants (88%) used a smartphone, but few (31%) played games on their phone. Usability rating was 6.2±0.7 (1-7 scale) and most participants reported task difficulty was just right (75%-81%). All tasks showed lower performance with increasing age and brick drop inhibition and space imposters also correlated with education (p's<0.05). Concentration performance correlated with UDS3 memory measures (p's<0.05, Figure 2). Brick drop performance correlated with measures of attention and executive functioning (p's<0.05, Figure 3). Space imposters correlated with the Digit Symbol Substitution Test (p<0.001, Figure 4). Some participants performed similarly across AM and PM sessions, while others showed differential performance. Initial change scores from the first to second session and from the 2nd to the last sessions did not show significant change. This pilot study shows acceptability and usability of the app for at-home use. Performance across measures indicate initial reliability and validity of mCAPP with continued data collection with a larger sample needed.

Effects of crime scene contaminants on surface-enhanced Raman analysis of hair.

Forensic analysis of hair is important as hair is one of the most commonly examined forms of trace evidence found at crime scenes. A growing body of evidence suggests that surface-enhanced Raman spectroscopy (SERS), a label-free and non-destructive analytical technique, can be used to detect and identify artificial colorants present on hair. However, hair collected at crime scenes is often contaminated by substances of biological and non-biological origin present at such locations. In this study, we investigate the extent to which four contaminants, saliva, blood, dirt, and bleach can alter the accuracy of SERS-based detection and identification of both permanent and semi-permanent colorants present on hair. Our findings show that saliva and dirt reduce the intensity of the colorants' signals but do not obscure their detection and identification. At the same time, an exposure of the colored hair to bleach or the presence of blood eliminates SERS-based analysis of artificial dyes present on such samples. We identified the procedure that can be used to remove blood contamination, which, in turn, enables identification of the hair colorants on such pre-cleaned samples. However, bleach treatment irreversibly eliminates SERS-based detection of artificial colorants on hair. These findings expand our understandings about the potential of SERS in forensic investigation of colorants on trace hair evidence.

THE CONCEPT OF "CHARACTER'S SPEECH" IN TRANSLATION AND ITS THEORETICAL BASICS

The study of character speech in fiction has long been a problem in science. There has been a lot of research in this area, especially in Uzbek and Russian translation, and major scientific works have been created. Just as the absence of some errors and inaccuracies in the translation does not mean that the quality of the translation is high, their presence does not mean that the translation is of poor quality and does not meet the requirements. The researcher who seeks to study a translation should focus on the power of influence inherent in the translation, and whether the original ideological and artistic value is fully preserved. It is much easier to translate phraseological units literally than to find an alternative to them in the language being translated, or to take a creative approach to the subject and create a new phrase that matches the original phrase and fully expresses its meaning. However, if a clear, artistic, and semantic alternative to the original phrase is in the language being translated, and the translator does not take this into account, it is a shortcoming. It's about finding an alternative to that phrase and using it in its place. The main purpose of the article is to analyze the phraseological units, focusing on the culture, history, philosophy and mythology of the studied language. The use of phraseology in character speech in translation has not been specifically studied in comparative linguistics or translation studies. This determines the relevance of this topic. Anthrocentric focus on phraseological units in Hindi and Uzbek languages, identification of national color and figurative features of phraseology and methods of their translation.