Color Classification Research Articles

A new approach to broaden the range of eye colour identifiable by IrisPlex in DNA phenotyping

IrisPlex system represents the most popular model for eye colour prediction. Based on six polymorphisms this model provides very accurate predictions that strongly depend on the definition of eye colour phenotypes. The aim of the present study was to introduce a new approach to improve eye colour prediction using the well-validated IrisPlex system. A sample of 238 individuals from a Southern Italian population was collected and for each of them a high-resolution image of eye was obtained. By quantifying eye colour variation into CIELAB space several clustering algorithms were applied for eye colour classification. Predictions with the IrisPlex model were obtained using eye colour categories defined by both visual inspection and clustering algorithms. IrisPlex system predicted blue and brown eye colour with high accuracy while it was inefficient in the prediction of intermediate eye colour. Clustering-based eye colour resulted in a significantly increased accuracy of the model especially for brown eyes. Our results confirm the validity of the IrisPlex system for forensic purposes. Although the quantitative approach here proposed for eye colour definition slightly improves its prediction accuracy, further research is still required to improve the model particularly for the intermediate eye colour prediction.

Colour-concept association formation for novel concepts

ABSTRACT Colour-concept associations influence fundamental processes in cognition and perception, including object recognition and visual reasoning. To understand these effects, it is necessary to understand how colour-concept associations are formed. It is assumed that colour-concept associations are learned through experiences, but questions remain concerning how association formation is influenced by properties of the input and cognitive factors. We addressed these questions by first exposing participants to colour-concept co-occurrences for novel concepts (“Filk” and “Slub” alien species) using a category learning task. We then assessed colour-concept associations using an association rating task. During alien category learning, colour was a noisy cue and shape was 100% diagnostic of category membership, so participants could ignore colour to complete the task. Nonetheless, participants learned systematic colour-concept associations for “seen” colours during alien category learning and generalized to “unseen” colours as a function of colour distance from the seen colours (Experiment 1). Association formation not only depended on colour-concept co-occurrences during alien category learning, but also on cognitive structure of colour categories (e.g., degree to which an observed red colour is typical of the colour category “red”) (Experiment 2). Thus, environmental and cognitive factors combine to influence colour-concept associations formed from experiences in the world.

The art of painting rainbows—between color science and painter's practice

AbstractWe present the first quantitative analysis of representing rainbow colors in historical paintings from Antiquity to the twentieth century. Using high‐resolution photographs of art works we determined and analyzed percentages of areas from painted rainbows covered by different color categories. Our results are the first to quantitatively show that up to the nineteenth century, painters generally did not follow contemporary color theories when painting rainbows. The only evidence of an influence from color theory on painted rainbows is the use of a color scheme consisting of three primary and three secondary colors as described by d'Aguilon (1613) and others. For painting rainbows, this scheme was used for the first time in the early nineteenth century, rising to 6% of all paintings created during that century, and reaching 24% in the twentieth century. We conclude that in the vast majority of paintings, individual color choices by the painter were decisive. Additionally, we identified a few weak trends: (1) color combinations in painted rainbows over time gradually become slightly more similar to the color proportions in natural rainbows; (2) from the Renaissance onward, circa 35%–40% of the painted rainbows consist of orange, yellow and green, sometimes with white as well. This trend may originate from Antiquity; (3) Already from Antiquity a white band was used as an artistic but unrealistic device to create the impression of a luminous arc. This trend reached its maximum in popularity in Baroque, but it is still used in many painted rainbows up to the twentieth century.

Knowledge Assessment of Biomedical Waste Management of Dental Materials in Dakshina Kannada

Abstract Aims and Objective The present survey was done to assess the knowledge, awareness, and practice of undergraduate students, postgraduate students, and practitioners about disposal of common biomedical wastes and materials used in dentistry regularly. Materials and Methods The survey (23 questions) was conducted on 162 participants categorized as undergraduates (n = 54), postgraduates (n = 54), and practitioners (n = 54) to assess their knowledge, awareness, and practice. Results When questioned whether they were aware of disposal of waste according to color categories, 69.8% were aware of it, 2.5% were not, and 27.8% were not sure of the color-coded categories. When subjected to statistical analysis, the data were found to be statistically significant (p = 0.017). Only 58% were aware that the number of categories of biomedical waste management is divided into four categories based on new regulations. There was statistical significance between the scores of undergraduates and practitioners (p = 0.046). Conclusion There is a gap between knowledge and implementation of segregation of waste at source; certified carrier service should make sure all the wastes are segregated from the dental practitioner's premise with an update on dental materials segregation for dental clinics and laboratories. Frequent updates of biomedical management of waste rules should be performed by the certified carrier.

"Comparison of Color Classification Using Computer Vision and Deep Neural Network"

Research on artificial intelligence and machine learning is currently ongoing and is focused on real-world problems. Machine learning is used by computers to make predictions based on the provided data set or existing knowledge. The main goal of our project is to use machine learning to categorize different colors while separating CNN from computer vision. In this work, we used supervised learning to categorize different hues using a binary classification approach. Color detection is the technique of identifying a color. In this scenario, humans can recognize the hue and choose with ease. A computer, however, cannot quickly recognize color. It is challenging to get a computer to quickly detect the color. Given that, we decide to pursue this initiative. Pandas, OpenCV, and the Naive Bayes algorithm are all used in Python. Naive Bayes classifiers are models that assign category labels to issue occurrences that are represented as vectors of feature values, where the category labels are selected from a finite set. There isn't a single method for training these classifiers; rather, there is a family of algorithms built on the premise that, given a category variable, the value of one feature is independent of the value of the other feature. Open-Source Computer Vision Library OpenCV was designed to be computationally effective and with a major emphasis on real-time applications. specialized video encoding for the cloud. Panda may be a platform that runs in the cloud and provides infrastructure for encoding audio and video.

Цветовые системы как способ репрезентации категорий цвета

Color systems have a rich history and are widely used in industry, art and science, but philosophical and cultural studies of color are not sufficiently presented. The purpose of the article is to determine the specifics of color representation in the form of a color system. The author considers the concepts of “representation” and “categorization” as key operations to identify relationships between color categories, thereby providing the basis for the formation of a color system. The results of the analysis of the author's color systems and modern color spaces are presented in the context of the cultural and historical development of society. The connection between the perceptual and nominative aspects of the category of color is shown on the examples of studies of color names in Russian and English. By considering various color systems, it has been established that visual representation is a complex process of understanding the color categories, which results in a certain geometric representation of the relationship between colors of various origins: paints, dyes, and electromagnetic radiation. Visual representatives have evolved due to qualitative changes in the subject and object of cognition. On the one hand, the form and content of color systems changed due to the subject’s historical and social development. On the other hand, the emergence of new shades, as well as development of visualization systems in technology and color printing have contributed to the change of the form and content of color systems.

Production and Organoleptic Test of Hand Sanitizer Nanogold, Nanosilver, and Eucalyptus Oil in the Pandemic Covid-19

Hand sanitizer is an antiseptic that contains alcohol and other active ingredients that can prevent and inhibit microbial infections. The use of hand sanitizer has increased during the COVID-19 pandemic. Hand sanitizer used continuously can cause damage to the skin because it contains harsh chemicals, such as alcohol and peroxide. Therefore, there needs to be innovation in hand sanitizer during the Covid-19 pandemic, ranging from antioxidants, antimicrobials to antivirals. The purpose of this study was to produce and conduct organoleptic tests on hand sanitizer with the addition of nanogold, nanosilver, and eucalyptus oil. The formulations used in the manufacture of this hand sanitizer have been in accordance with those set by WHO with the addition of the active ingredients nanogold, nanosilver, and eucalyptus oil. The addition of nanogold functions as an antioxidant to keep the skin healthy. Nanosilver and eucalyptus oil function as an antimicrobial to antiviral, which is suitable in the formulation of hand sanitizer during the COVID-19 pandemic. The results of the research at the organoleptic test stage showed that hand sanitizer with the addition of nanogold, nanosilver, and eucalyptus oil were preferred by the panelists with an average value of 3.45 in the color category, 3.95 in the fragrance category, and 3.50 in the absorption level category.

Frankly, My Error, I Don't Give a Damn: Retrieval of Goal-Based but Not Coactivation-Based Bindings after Erroneous Responses.

Previous studies demonstrated binding and retrieval of stimuli and correct responses even for those episodes in which the actual response was wrong (goal-based binding and retrieval). In the current study, we tested whether binding based on a co-activation of stimuli and erroneous responses occurred simultaneously with goal-based binding, which could have been masked by a more efficient retrieval of goal-based bindings in previous studies. In a pre-registered experiment (n = 62), we employed a sequential prime-probe design with a three-choice colour categorisation task. Including three different responses in the task allowed us to conduct separate tests for stimulus-based episodic retrieval of either the correct response (goal-based) or of the actual erroneous response (coactivation-based) after committing an error. Replicating previous findings, our study provides support for goal-based binding of stimuli and correct responses after errors, while showing that there is no independent coactivation-based binding of the erroneous response itself.

Effect of Product Color Lightness on Hedonic Food Consumption: The Regulatory Role of Hedonic and Extrinsic Value

Food colorants mean to evoke positive feelings about the product on consumers and to increase the pleasure of consumption. Much of the research on packaging and food coloring has been carried out on the effects of certain hues and color categories. The neglected subject of product color lightness and saturation necessitated this research. In the study conducted on hedonic foods, which is the product group in which food colorants are most used, the effect of product color lightness on hedonic consumption, extrinsic value behavior, which is characterized as the tendency to consume snacks, and the regulatory role of hedonic value perception factors, which are defined as the hedonic benefit provided to consumers were examined.

Green Supply Chain Performance Measurement using Green SCOR Model in Agriculture Industry: A Case Study

The agriculture industry has proliferated in the last decades, increasing the envi­ron­mental footprint. Several development concepts include integrating the ecological aspect into the supply chain to reduce environmental degradation. In implementing the idea, companies in the agriculture industry need to evaluate their performance in the environmental area. This measure­ment uses the Green Supply Chain Operations Reference (GSCOR) Model that provides its entire supply chain aspect. This study demonstrates that the enable parameter criterion, which shows the magnitude of employee management toward environmental requirements, significantly impacts supply chain performance. Other criteria are also critical, such as a plan that considers every entity's usage and a source that considers the supply of the entities. The performance measurement produces a 6.357 value in the yellow color category with an average condition in the company. It produces three key performance indicators (KPI), such as water usage, percentage hazardous materials in inventory, and percentage hazardous waste, with a red classification that should be improved.

The elephant in the room: teaching race in consensus

ABSTRACT Clear instruction and dialogue on race are necessary as social workers desire to be part of freeing the world of racial bias. The social work profession can adopt and support clear definitions that strengthen the educational content being produced in the classroom as it relates to teaching race in consensus. This qualitative study examines the varying degrees of how licensed social workers define race. The sample consisted of 82 licensed social workers. Majority of the participants were White (69%; n = 57), female (92%; n = 76), 21 to 35 years of age (42%; n = 35), Licensed Master level social workers (68%; n = 56) who had been in practice for three to eight years (46%; n = 38). Researchers used analytic strategies to identify words or phrases from participant responses of how they defined ‘race.’ The responses for race definitions were collapsed into five thematic concepts: Culturally Based, Personal Identification, Color Classification, Biological Essentialism, and Origin. The overall findings support a limited ability of helping professionals to clearly define race for themselves or their clients.

Categorical bias as a crucial parameter in visual working memory: The effect of memory load and retention interval

Visual information can be stored as continuous as well as categorical representations in visual working memory (VWM) to guide subsequent behavior. Yet it is still unclear what determines whether VWM is represented as continuous or categorical information, or as a mix of both. Recent studies have shown that color VWM representations adjust flexibly depending on the number of memory items as well as the duration that these items need to be maintained for. The current study aims to extend and replicate these crucial effects. In a delayed estimation task, participants memorized one to four colored objects presented at different spatial locations, followed by a delay of 100, 500, 1000, or 2000 msec. Next, a probe indicated the location of the color that participants needed to report. We measured the extent to which responses were biased in the direction of prototypical colors. Crucially, we implemented this categorical bias in an extension to the classic mixture model (Zhang & Luck, 2008) in which the center of the error distribution is a crucial parameter that characterizes the extent to which VWM is biased by color categories. We found that VWM shows a strong categorical bias in all cases, and that this bias increases with increasing memory load; strikingly, this effect of memory load on categorical bias is stronger at longer intervals (1000 msec or longer), as compared to shorter intervals, yet it peaks for intermediate memory loads as opposed to the highest memory load. Overall, our results suggest that when visual information needs to be maintained for one second or longer, VWM becomes more reliant on categorical representations as memory load increases.

Radiological characterization of nephrite produced in Xinjiang, Northwest China

The specific activities of 226Ra, 232Th and 40K of seven different color classification of nephrite samples from Xinjiang of China were measured by gamma spectrometry, and their radiological hazard indices such as radium equivalent (Ra eq), external hazard index (Hex ), internal hazard index (H in), alpha index (I α), absorbed dose rate (D) and annual effective dose rate (AED) were investigated and assessed. According to the experimental results, the specific activity level of 226Ra, 232Th, 40K in all nephrite samples was between 13.56 and 116.39 Bq·kg−1 with an average of 44.72 Bq·kg−1, between 2.19 and 10.89 Bq·kg−1 with an average of 4.90 Bq·kg−1, and between 21.06 and 94.32 Bq·kg−1 with an average of 52.24 Bq·kg−1, respectively. The mean values of radiological indicators, such as Ra eq, Hex , H in, I α and AED were 55.75 Bq·kg−1, 0.15, 0.27, 0.22, 0.16 mSv·y-1 (worn) and 0.13 mSv·y−1 (indoor), respectively, are lower than the international recommended value. The values of D range from 10.13 to 54.46 nGy·h−1 with an average of 25.80 nGy·h−1. In the end, it can be concluded that the use of the seven different color categories of nephrites as ornament is considered to be safe and unlikely to cause any significant biological radiation hazard to the public.

Can Forel–Ule Index Act as a Proxy of Water Quality in Temperate Waters? Application of Plume Mapping in Liverpool Bay, UK

The use of ocean colour classification algorithms, linked to water quality gradients, can be a useful tool for mapping river plumes in both tropical and temperate systems. This approach has been applied in operational water quality programs in the Great Barrier Reef to map river plumes and assess trends in marine water composition and ecosystem health during flood periods. In this study, we used the Forel–Ule colour classification algorithm for Sentinel-3 OLCI imagery in an automated process to map monthly, annual and long-term plume movement in the temperate coastal system of Liverpool Bay (UK). We compared monthly river plume extent to the river flow and in situ water quality data between 2017–2020. The results showed a strong positive correlation (Spearman’s rho = 0.68) between the river plume extent and the river flow and a strong link between the FUI defined waterbodies and nutrients, SPM, turbidity and salinity, hence the potential of the Forel–Ule index to act as a proxy for water quality in the temperate Liverpool Bay water. The paper discusses how the Forel–Ule index could be used in operational water quality programs to better understand river plumes and the land-based inputs to the coastal zones in UK waters, drawing parallels with methods that have been developed in the GBR and Citclops project. Overall, this paper provides the first insight into the systematic long-term river plume mapping in UK coastal waters using a fast, cost-effective, and reproducible workflow. The study created a novel water assessment typology based on the common physical, chemical and biological ocean colour properties captured in the Forel–Ule index, which could replace the more traditional eutrophication assessment regions centred around strict geographic and political boundaries. Additionally, the Forel–Ule assessment typology is particularly important since it identifies areas of the greatest impact from the land-based loads into the marine environment, and thus potential risks to vulnerable ecosystems.

Robust categorical color constancy along daylight locus in red-green color deficiency.

Categorical color constancy in normal trichromats has been found to be very robust in real scenes. In this study, we investigated categorical color constancy in red-green dichromats and anomalous trichromats. Eight dichromats (two protanopes and six deuteranopes), eight anomalous trichromats (four protanomalous and four deuteranomalous trichromats), and eight normal trichromats sorted 208 Munsell matte surfaces into Berlin and Kay's basic color categories under D65 illuminant, F illuminant with correlated color temperature 4200 K, and TL84 illuminant with correlated color temperature 2700 K. Color constancy was quantified by a color constancy index. The results showed that the constancy index of dichromats (0.79) was considerable and significantly lower than that of normal trichromats (0.87) while that of anomalous trichromats (0.84) was not. The impairment of color constancy performance in dichromats was expected to be caused by their large intra-subject variabilities in color naming. The results indicate robust categorical color constancy along daylight locus in red-green dichromats and anomalous trichromats, which might be contributed by cone adaptation mechanism and be independent of color discrimination mechanism. It suggests that the color categorization by color vision deficient subjects can be reasonable without any assistants of artificial equipment in daily life under sunlight and common illuminations.

Application of Intelligent Image Color Technology in Teaching Chinese Painting Color

This paper presents an in-depth analysis and research on the application of intelligent image color technology in teaching Chinese painting color. A feature reorganization-based approach to Chinese painting image style migration is proposed. Using the connection between depth features in neural networks and image semantics, the artistic style expression method of traditional Chinese painting is combined with existing methods to match the feature matrix in the generative network by feature reorganization, and finally, the migrated image is generated using a decoder. The texture of the target image is input to the generator as an additional condition, and the discriminator is changed to a relativistic discriminator to build a new generative adversarial network. To demonstrate the effectiveness of the method, a large number of Chinese ink and wash style landscape paintings and natural landscape photos are used as experimental data. The experimental results show that the obtained generated images after style migration are 8% better in realism and quality. An image color intelligent analysis system was designed, which integrates a color classification module, a pigment analysis module, an output saving module, and a pigment sample library to achieve the automatic classification of different color areas in Chinese painting, and ultimately allows a judgment to be made on the most likely pigment to be used for each type of color. In the teaching of Chinese painting, the system can transform the traditional “ear-listening” color transmission through vision into eye perception, which makes the teaching of Chinese painting vivid, arouses the enthusiasm of the classroom, and greatly promotes the color teaching of Chinese painting.

A Review of Safety Risk Theories and Models and the Development of a Digital Highway Construction Safety Risk Model

This study conducts a systematic review of safety risk models and theories by summarizing and comparing them to identify the best strategies that can be adopted in a digital ‘conceptual’ safety risk model for highway workers’ safety. A mixed philosophical paradigm was adopted (that used both interpretivism and post-positivism couched within inductive reasoning) for a systematic review and comparative analysis of existing risk models and theories. The underlying research question formulated was: can existing models and theories of safety risk be used to develop this proposed digital risk model? In total, 607 papers (where each constituted a unit of analysis and secondary data source) were retrieved from Scopus and analysed through colour coding, classification and scientometric analysis using VOSViewer and Microsoft Excel software. The reviewed models were built on earlier safety risk models with minor upgrades. However, human elements (human errors, human risky behaviour and untrained staff) remained a constant characteristic, which contributed to safety risk occurrences in current and future trends of safety risk. Therefore, more proactive indicators such as risk perception, safety climate, and safety culture have been included in contemporary safety risk models and theories to address the human contribution to safety risk events. Highway construction safety risk literature is scant, and consequently, comprehensive risk prevention models have not been well examined in this area. Premised upon a rich synthesis of secondary data, a conceptual model was recommended, which proposes infusing machine learning predictive models (augmented with inherent resilient capabilities) to enable models to adapt and recover in an event of inevitable predicted risk incident (referred to as the resilient predictive model). This paper presents a novel resilient predictive safety risk conceptual model that employs machine learning algorithms to enhance the prevention of safety risk in the highway construction industry. Such a digital model contains adaptability and recovery mechanisms to adjust and bounce back when predicted safety risks are unavoidable. This will help prevent unfortunate events in time and control the impact of predicted safety risks that cannot be prevented.

Quality Of Dumpling Crackers Produced From Purple Sweet Potato Flour Substitution

This study aims to analyze the effect of purple sweet potato flour substitution on the quality of dumpling crackers with the addition of purple sweet potato flour as much as 5%, 10%, and 15%. This research was conducted at the Catering Workshop in August 2021. This type of research is a pure experimental study with a completely randomized design (CRD) method consisting of 4 treatments and 3 rehash. The object of this research is dumpling crackers with the addition of 5%, 10%, and 15% purple sweet potato flour. The independent variable in this study was purple sweet potato flour. The dependent variable of this research is the quality of the dumpling crackers produced including the shape, color, aroma, texture, and taste. Analysis of the data obtained from the organoleptic test involving 5 expert panelists, namely the lecturer of Family Welfare Science, Padang State University. The data analysis technique used is Analysis of Variance (ANOVA) to analyze sensory test data. The data that has been obtained is then tabulated in tabular form and analyzed according to the test of each data. Then the data analysis is carried out, if the data obtained by Fcount is greater than Ftable, then it is continued with Duncan's test. The results showed that the substitution of purple sweet potato flour had an effect on color, and had no effect on shape, aroma, texture, and taste. Based on the results, the best research was found in the treatment (X3) 15% with the categories of neat shape, purple color, fragrant aroma, crunchy texture, and savory taste, therefore for further research, it is recommended to use purple sweet potato flour with 15% substitution which produces good quality.

Pengaruh Pemanfaatan Masker Alami Berbasis Yoghurt dan Air Cucian Beras terhadap Kesehatan Kulit Wajah

Staying in the house for a long time can cause facial skin to become dry and oily. The use of a face mask with yogurt and rice water which is rich in nutrients can help with dry or oily facial skin problems. The purpose of this study was to analyze the effect of natural masks based on yogurt and rice washing water on facial skin health. In this study, a quasi-experimental method was used. In this study, the dependent variable was facial skin and the independent variable was yogurt and rice washing water. The formulation of yogurt and washing water is 2:1. This mask was applied to 19 respondents who were observed before and after using a yogurt face mask and washing water. The skin moisture meter is used, namely the Ckeyin Fluorescence Detector, the average initial humidity is 41.56 and the end is 56.6, which means that facial skin moisture increases and the initial oil level is 31.21 and the end is 21.08, which means the facial oil level decreases. With the help of SPSS set the value of sig. below 0.05 and obtained sig. 0.00. The difference in the average initial and final results indicates that there is an effect of a natural face mask made from yogurt and rice washing water on the moisture and oil content of the face. Based on the organoleptic test through the regression test, the mean and standard error results for the aroma category are 3 ± 0,597; color category 3 ± 0.229; and texture category 3 ± 0.452. The data shows that respondents like the mask formula because it is obtained on a scale of 3 out of 4 and it can be concluded that natural face masks made from yogurt and rice washing water can improve facial skin health.

Örnek Bölütlemesi ile Nesne ve Renk Sınıflandırması

Görüntü üzerinde nesne tespit ve sınıflandırma uygulamaları görüntü işleme alanında ele alınan temel konulardandır. Otonom araçlar ve görsel takip sistemleri gibi popüler uygulamalarda ihtiyaç duyulan nesne tespit ve sınıflandırma işlemlerinin gerçekleştirilmesinde evrişimsel sinir ağları, hesaplama performansı (hızı) ve başarımı ile öne çıkmaktadır. Ancak nesne tespit ve sınıflandırma işlemleri aynı tip nesnelerin renk gibi farklı özellik çıkarımlarından yoksun olmaktadır. Bu durumun temelinde ise nesne tipi aynı olsa da her bir rengin yeni bir sınıf olarak ağa tanıtılması gerekliliğidir. Tespit edilen nesnenin renk bilgisini edinmenin bir diğer yolu ise nesneye ait görüntüyü piksel seviyesinde işlemektir. Piksel seviyesinde yapılacak işlemlerin doğruluğunu arttırmak için nesne tespitinin yanında bölütme işlemi de yapılarak tespit edilen nesnenin sınırlarını net olarak belirlemek gereklidir. Tespit edilen nesnenin rengi tespit edilen nesne sınırları içerisindeki piksel yoğunluk değerleri ile sınıflandırılabilir. Bu çalışmada evrişimsel sinir ağları ile gerçekleştirilen örnek bölütlemesi sonrası piksel bilgilerine dayalı renk sınıflandırması yapılarak nesnelerin sınıflarının yanı sıra renkleri de tespit edilebilmiştir. Ortaya konulan yaklaşımın başarısı deneysel olarak sınanmış ve etkin bir yöntem sunularak literatüre katkıda bulunulmuştur.