Color Classification Research Articles

Tomatoes Are Red: The Perception of Achromatic Objects Elicits Retrieval of Associated Color Knowledge.

When preparing to name an object, semantic knowledge about the object and its attributes is activated, including perceptual properties. It is unclear, however, whether semantic attribute activation contributes to lexical access or is a consequence of activating a concept irrespective of whether that concept is to be named or not. In this study, we measured neural responses using fMRI while participants named objects that are typically green or red, presented in black line drawings. Furthermore, participants underwent two other tasks with the same objects, color naming and semantic judgment, to see if the activation pattern we observe during picture naming is (a) similar to that of a task that requires accessing the color attribute and (b) distinct from that of a task that requires accessing the concept but not its name or color. We used representational similarity analysis to detect brain areas that show similar patterns within the same color category, but show different patterns across the two color categories. In all three tasks, activation in the bilateral fusiform gyri ("Human V4") correlated with a representational model encoding the red-green distinction weighted by the importance of color feature for the different objects. This result suggests that when seeing objects whose color attribute is highly diagnostic, color knowledge about the objects is retrieved irrespective of whether the color or the object itself have to be named.

МИФОПОЭТИКА ВИНА КАК ИМАГИНАТИВНЫЙ АБСОЛЮТ ЧЕЛОВЕЧЕСКОГО БЫТИЯ

the article introduces a new concept at the intersection of philosophy, aesthetics, literary criticism, history, oenology and branding: mythopoetics. Mythopoetics of wine is understood as a special method of wine analytics that takes the perception of wine beyond the narrow classification of color, bouquet and taste with elements of terroir into the area of image formation: the harmony of historical fact, myth, terroir, poetry, symbol, which are quintessed in a certain wine brand. The article is a continuation of the efforts of José Ortega y Gasset, Massimo Dona, Eric Asimov, Rauf and Alexei Akchurinov in creating an approach to the philosophical understanding of wine. Wine is defined as an essential element of human intellectual and emotional life, firmly embedded in religious practices, secular culture and various genres of art. An important fact is the strong incorporation of wine into the economic structure, especially in the great wine regions of France, Italy, Spain, and Germany: from wine tastings and wine tours to the formation of a solid segment of the alcohol market in the wholesale and retail trade networks. Mythopoetics of wine allows creating its legendarium, project myths about life and death, beauty and ugliness, harmony and chaos onto a specific brand of wine, form the image of a particular wine producer, enhancing the tasting value of wine, increasing interest in thecourtliness of drinking wine, stimulating the tourist development of various regions, bringing the topic of wine to the level of the most pressing problems for interdisciplinary research, both in the field of fundamental knowledge and at the level of applied results.

Weighted Hermite Variable Projection Networks for Classifying Visually Evoked Potentials.

The occipital cortex responds to visual stimuli regardless of a patient's level of consciousness or attention, offering a noninvasive diagnostic tool for both ophthalmologists and neurologists. This response signal manifests as a unique waveform referred to as the visually evoked potential (VEP), which can be extracted from the electroencephalogram (EEG) activity of a human being. We propose a trainable VEP representation to disentangle the underlying explanatory factors of the data. To enhance the learning process with domain knowledge, we present an innovative parameterization of classical Hermite functions that effectively captures VEP pattern variations arising from patient-specific factors, disorders, and measurement setup influences. Then, we introduce a differentiable variable projection (VP) layer to fuse Hermite basis function expansions (BFEs) of VEP signals with machine learning (ML) approaches. We prove the existence of an optimal set of parameters in the least-squares sense, assess the representation power of such layers, and calculate their analytical derivatives, which allows us to utilize backpropagation for training. Finally, we evaluate the effectiveness of the proposed learning framework in VEP-based color classification. To achieve this, we have designed a novel measurement system dedicated to intraoperative clinical use cases, which presents new ways for patient monitoring during neurosurgical procedures.

Highly Efficient Machine Learning Approach for Automatic Disease and Color Classification of Olive Fruits

Highly Efficient Machine Learning Approach for Automatic Disease and Color Classification of Olive Fruits

Evaluation of the Correlation Between Nasal Secretion ECP-MPO Test Papers and Immune Markers in Subcutaneous Immunotherapy of Dust Mites.

Up to now, there is no generally accepted biomarker to indicate the clinical response of immunotherapy. This study mainly analyzed the correlation between eosinophil cationic protein-myeloperoxidase (ECP-MPO) test papers and other immunotherapy indices in subcutaneous immunotherapy of dust mites and to explore whether the test paper can be used as an auxiliary index to quickly evaluate the efficacy of immunotherapy. This study included 53 participants who received subcutaneous immunotherapy at the allergy clinic of Renmin Hospital of Wuhan University and 28 control participants. Six visits were conducted during a prospective study over one year. The results of the ECP-MPO test paper, nasal secretion eosinophil smear and count, nasal secretion ECP concentration, and clinical symptom scores were collected during five follow-up visits after the start of subcutaneous immunotherapy. Th1/Th2/Th17 cytokines, chemokines, IgE, IgG4 against dust mite components, and ECP concentrations were detected in the serum of participants at baseline, six months, and one year after subcutaneous immunotherapy. The ECP test paper is not only easy to operate, but also can effectively and quickly detect the concentrations of ECP in the nasal secretion and diagnose allergic rhinitis. Symptom score is an important index for evaluating clinical immune efficacy, during subcutaneous immunotherapy, the ECP test paper showed a positive correlation with the symptom score. Simultaneously, during immunotherapy, the changes in the chromogenic grading of the test paper were synchronized with the changes in inflammatory cytokines and eosinophilic chemokines in Th2 cells of serum dust mite IgE. The sIgG4 against dust mites weakly negatively correlated with the concentration of ECP in nasal secretions and the color classification of the ECP test paper. The ECP-MPO test paper has a certain correlation with subcutaneous immunotherapy markers of allergic rhinitis, indicating that the ECP test paper may become an auxiliary biomarker to replace other complex laboratory tests.

Evaluation of genomic factors and early childhood stimulation on intelligence in children from a Brazilian birth cohort: The primary role of independent factors

BackgroundChildhood cognitive abilities are a predictor of health outcomes and adult income potential. Identifying factors associated with childhood intelligence and their interactions is essential in behavioral research. We assessed the impact of genetic variants and early child stimulation (ECS) on child intelligence and examined their possible interaction as potential modifiers of IQ in a population-based longitudinal study. MethodsParticipants of the 2004 Pelotas Birth Cohort study (N = 4231) underwent intelligent quotient (IQ) by WISC-III assessment at 6 years of age. At 24 and 48-months, mothers answered five ECS marker questions, whose sum was used to create a score. The polygenic score for intelligence (IQ-PGS) was constructed from the GWAS-weighted estimate of cognition. Association was assessed using multiple linear regression models adjusted for maternal, family, and child confounding variables. To explore the possible influence of skin color and ethnoracial classification, the regression models were stratified according to the skin color variable, as a sensitivity analysis. ResultsIn the adjusted analysis, IQ-PGS (β = 0.79, 95% confidence interval [95% CI] 0.26;1.31) as well as ECS (β = 2.34; 95% CI: 1.76;2.92) were associated with IQ in this sample. The association between IQ-PGS and IQ was significant only in the white Brazilian group in the sensitivity analysis. However, there was no interaction between IQ-PGS and ECS on IQ (p(IQ-PGS x ECS) = 0.46). ConclusionsECS did not modify the impact of genetic potential on intellectual development during childhood, suggesting that genetic factors and ECS exert independent effects on the IQ levels of children.

퍼스널 컬러 신체 색 분류 실습을 위한 컬러 제안

퍼스널 컬러 신체 색 분류 실습을 위한 컬러 제안

Brightness and hue judgment of dyed hair color

This study aims to investigate human hair color perception through two empirical studies in the context of colored hair. The preliminary study was intended to establish a numerical representation of perceptually meaningful brightness levels. It identified that the brightness level was proportional to the power of 0.766 of L*. In the visual assessment, participants (N = 47) categorized 246 hair color samples into eight color hue groups aligned with the Munsell system. Hue judgment was conducted by visually comparing dyed hair tresses with natural black hair. Based on the L*, a*, and b* values of hair tresses and visual assessments thereof, we observed the ranges of hue categories for hair color alongside the brightness levels. Additionally, the differences between the Munsell hue names and the assessment results were compared. Predominantly influenced by the dark brown hair color, the neutral orientation was shifted to the first quadrant of the a*-b* plane. The study contributes to an understanding of human hair color perception and provides insights into color categorization and labeling, especially when the context is confined.

Prototype of a device adaptable to walk-sticks with a system to communicate the visible spectrum to blind people.

This project aims to design and build a device adaptable to canes for blind people that allows them to know the color of everyday objects through a tactile response. The prototype of the device identifies the colors by means of the TCS3200 color sensor that, when detecting the light reflected by the objects, converts it into an electrical signal to later process it with the Arduino Nano microcontroller and thus generate a tactile response using a servomotor. For this purpose, mechanical and electronic designs of the color recognition and representation systems using the Feelipa color code were developed, and a functional prototype of the adaptive device that identifies primary colors was developed. Algorithms for the classification of secondary colors from their RGB values continue to be developed, and the mechanical designs are being improved to take into account aesthetic and ergonomic aspects that facilitate their use

Dynamic Oppositional Symmetries for Color, Jungian and Kantian Categories

This paper investigates some classical oppositional categories, like synthetic versus analytic, posterior versus prior, imagination versus grammar, metaphor versus hermeneutics, metaphysics versus observation, innovation versus routine, and image versus sound, and the role they play in epistemology and philosophy of science. The epistemological framework of objective cognitive constructivism is of special interest in these investigations. Oppositional relations are formally represented using algebraic lattice structures like the cube and the hexagon of opposition, with applications in the contexts of modern color theory, Kantian philosophy, Jungian psychology, and linguistics.

Rainbow Colormaps: What are they good and bad for?

Guidelines for color use in quantitative visualizations have strongly discouraged the use of rainbow colormaps, arguing instead for smooth designs that do not induce visual discontinuities or implicit color categories. However, the empirical evidence behind this argument has been mixed and, at times, even contradictory. In practice, rainbow colormaps are widely used, raising questions about the true utility or dangers of such designs. We study how color categorization impacts the interpretation of scalar fields. We first introduce an approach to detect latent categories in colormaps. We hypothesize that the appearance of color categories in scalar visualizations can be beneficial in that they enhance the perception of certain features, although at the cost of rendering other features less noticeable. In three crowdsourced experiments, we show that observers are more likely to discriminate global, distributional features when viewing colorful scales that induce categorization (e.g., rainbow or diverging schemes). Conversely, when seeing the same data through a less colorful representation, observers are more likely to report localized features defined by small variations in the data. Participants showed awareness of these different affordances, and exhibited bias for exploiting the more discriminating colormap, given a particular feature type. Our results demonstrate costs and benefits for rainbows (and similarly colorful schemes), suggesting that their complementary utility for analyzing scalar data should not be dismissed. In addition to explaining potentially valid uses of rainbow, our study provides actionable guidelines, including on when such designs can be more harmful than useful. Data and materials are available at https://osf.io/xjhtf.

Development of an ANN Model for RGB Color Classification using the Dataset Extracted from a Fabricated Colorimeter

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under detection is one of the results of the proposed classifier. The work demanded the collection of about 5000 color codes which in turn were subjected to algorithms for training and testing. The open-source platform TensorFlow for ML and the open-source neural network library Keras were used to construct the algorithm for the study. The results showed an acceptable efficiency of the built classifier represented by an accuracy of 90% which can be considered applicable, especially after some improvements in the future to makes it more effective as a trusted colorimeter.

Contributed Session III: The naming and understanding of color: the Color Communication Game.

When a person views a color sample, they can usually provide a color term for it. But will that color term allow someone else to understand which sample was named? We examined color understanding using a Color Communication Game, in which one person (the "sender") names 30 color samples as in any color-naming study, then another person (the "receiver") chooses the sample they think the sender intended to communicate. The receiver cannot always guess the right sample, and no choice strategy will do better than randomly choosing among the samples the receiver called by that term. When 70 English-speaking dyads and 63 Somali-speaking dyads played the game, receivers did not perform randomly. Instead, they systematically chose "focal" samples near the centers of their color term distributions. When the senders' named samples were compared directly to the receivers' chosen samples, the systematic distribution of receiver choices revealed color categories, which appeared without any statistical analysis of the players' terms. Simulation of receiver choices based on senders' color names showed that both Somali-speaking and English-speaking participants knew more color terms than the ones they used in color-naming. Our Color Communication Game showed that color-naming experiments underestimate color understanding: people understand colors categorically, but they express colors using multiple synonymous color terms that are well-understood by others.

Poster Session II: Spillover effects of color discrimination training on color category boundaries and color appearance.

Perceptual learning refers to the increase in perceptual sensitivity that results from several days of training on a perceptual task. Although perceptual learning has been shown to be effective in a variety of perceptual tasks, few studies have examined perceptual learning in color perception. In this study, we investigated how color discrimination training at a base color affected various aspects of color perception for entire hues. The training consisted of five days of S color discrimination (300 trials/day) at either the negative or positive L-M base color, depending on the observer groups. Before and after the training, three types of color perception tests (color difference, unique hue, and color category boundary) were conducted for colors with various hues to examine the changes in color perception due to the training. The results showed that the color discrimination thresholds in the training decreased as expected with repeated trials. Interestingly, the training also affected the performance of the three types of tests; the perceived color difference around the training color tended to increase, and some of the unique hues and the color category boundaries shifted significantly toward the training color. These results suggest that only a few days of color discrimination training can spill over to the entire color space and induce distortion of the perceptual color space.

Poster Session II: Constrained color-sorting and the evolution of color terms.

Constrained color-sorting-the partition of a color palette into sequential numbers, n, of categories or "piles," n = {2, 3, …, N}-has been used to examine universal vs. culture-dependent processes that underlie color-term evolution. Here we use sorting to test two hypotheses related to these ideas. 1) If the constrained color sorts (CCSs) depend solely on universal processes that guide color-term evolution, then CCSs should be relatively insensitive to the exact composition of the color palette. 2) Otherwise, CCSs should at least be "optimal" (as measured by well-formedness: Regier et al., 2007). We report three studies, each using a different palette, where English speakers sorted the color samples into 2 … 6 piles; one study also included Somali speakers. All palettes included good examples of the Basic Color Categories (BCCs), but they differed in how hue, saturation, and lightness covaried. Results showed that pile-sorts reflected psychologically salient features of colors, which were mostly linked to BCCs. However, 1) the unfolding of sort-categories with increasing n differed greatly across palettes, and the order in which the sort-categories appeared always deviated from predictions based on classical theories of color term evolution; 2) pile-sorts were not optimal. Taken together, our results reveal strong dependence of CCSs on pre-existing lexical categories but only weak dependence on universal pre-linguistic representations of color related to color-term evolution.

How can a black horse have brown fur?

Hungarian equine coat colour-related terminology is a largely unresearched subject. Its unique situation as a rapidly developing jargon results in multiple nomenclatural systems used simultaneously in the specialised literature. The analysis of the nomenclatural systems’ inner structure suggests the usage of two differing methods of categorisation, one based on the visual properties of the subject, the other based on non-visual genetic factors of the described animal.
 The paper reviews the colour categories’ main traits that influence their categorisation process and compares them with the Hungarian equine coat colours’ characteristics. The comparison reveals that Hungarian equine coat colour terms are in many ways similar to the conventional colour terms, but due to their differences they create a separate colour-motivated class of specialised terms.
 The analytic part focuses on the different methods of categorisation present in the various nomenclatural systems in the equine coat colour related literature. It defines their main features and the underlying cause of their existence. It explores the division of the nomenclatural systems in accordance with their categorisation process and provides a detailed description of the traits of both traditional and modern nomenclatural systems discussing them on the basis of specific examples.
 The further part of the analysis explores the semantic relationship between the conventional colours and the equine coat colours in the context of equine coat colour terms sharing their forms with Basic Colour Terms.
 The research material consists of a database of Hungarian equine coat colour-related terms, gathered from a variety of zootechnical, sport and hobby literature and commonly available internet sources on the topic. The nomenclatural systems presented in the sources are visualised in the form of diagrams, illustrating the meaning of individual terms and the systems’ internal hierarchies.
 The presented findings showcase the complexity of the specialised colour-motivated terms and their difference from the conventional colour terms. They present how external, non-linguistic factors like the development of the natural sciences may influence the relatively hermetic colour-motivated terminology.

Dendrological key and species characterization of the subfamilies Mimosoideae and Caesalpinioideae, Tocantins, Brazil

Much of the vegetation in the State of Tocantins belongs to the Cerrado domain, widely known for hosting several species and forest families, among which the Fabaceae family stands out. However, misguided practices, incipient public policies, and few studies on the subject culminate in the loss of species found in the Cerrado of Tocantins. The present work aimed to elaborate a dichotomous key through dendrological characters. This study was carried out at the Federal University of Tocantins, Gurupi campus, which consisted of expeditions, and a collection of branches of tree species of the Fabaceae family in order to describe the vegetative characters. Seven species belonging to the Fabaceae family were analyzed, four from the Mimosoideae subfamily and three from the Caesalpinioideae subfamily. Characteristics such as bark color, extrafloral nectary, hairiness, and leaf classification allowed us to notice the differences between each sampled species. It can be inferred that the dendrological key of forest species of the Fabaceae family is fundamental for forest inventories and management plans of botanical species of the Cerrado Domain.

The Application of Traditional Hakka Clothing Cultural Elements in Modern Clothing Design in Western Fujian Based on Image Feature Extraction Technology

Abstract In this paper, by applying the SIFT algorithm to the color category, texture and shape features of the dress pattern image for feature extraction and eliminating the low contrast texture feature points of the Taylor series expansion, using the statistics of the grayscale covariance matrix as the texture features. Based on the SIFT algorithm, the SURF feature extraction algorithm is proposed. The ideas of the Hessian matrix, determinant value approximation (DoH) and integral image (IGI) classifier are integrated to construct the feature fusion algorithm. Finally, the experimental results of several algorithms are compared and analyzed by taking the traditional dress pattern of the western Fujian Hakka family as the experimental object. The results show that among all the fusion algorithms, the SURF+IGI algorithm achieves an average recognition rate of 0.8914, which is the best performance. And the AUC value of its test set is 0.9445 when the learning rate is 0.005. The research in this paper meets the demand of extracting and applying the cultural elements of the traditional dresses of the Hakka family in western Fujian in modern dress design.

Contingent capture by color is sensitive to categorical color perception.

Contingent capture (CC) theory postulates that attention can only be captured by top-down matching stimuli. Although the contingent capture of attention is a well-known and thoroughly studied phenomenon, there is still no consensus on the characteristics of the top-down template which guides the search for colors. We tried to replicate the classical contingent capture effect on color (Experiment 1) and then added linguistic processing to this perceptual effect (Experiment 2). In Experiment 1, attention was indeed captured by the cues of the same color as the target, while the cues of different colors were successfully ignored. In Experiment 2, the cue color was never identical to the target color but would either belong to the same linguistic category or not (i.e., linguistic matching and linguistic nonmatching cues). In both cases, cues were made to be equally perceptually distant from the target. Although, attention was captured by both cue types, the degree of capture was significantly higher for linguistic matching cues. Our research replicated the classic contingent capture effect but on color, and also demonstrated the effect of color categories in the search task. In short, we demonstrated the effect of color categories in the search task. Results show that the template for color search contains physical characteristics of color, as well as information about color category names.

Supra-Quantum Dot Assemblies to Maximize Color-Based Multiplexed Fluorescence Detection with a Smartphone Camera.

Photoluminescence (PL) imaging and bioanalysis with smartphone-based devices are of growing interest for point-of-care/point-of-need diagnostics. Strategies for maximizing sensitivity have been explored in this context, but color multiplexing has been very limited, with its maximum level unexplored. Here, we evaluated color multiplexing with smartphone-based PL imaging by using supra-nanoparticle assemblies of quantum dots (supra-QDs). These materials were prepared as composite colors that were tailored to the red-green-blue (RGB) color space of smartphone cameras by coassembling different ratios of R-, G-, and B-emitting QDs on a silica nanoparticle scaffold. The supra-QDs were characterized and used to label cell-sized objects that were measured under flow with a smartphone-based device. Each color followed an approximately linear trajectory in the RGB space, and training of support vector machine models enabled color classification with overall accuracies ≥87% for 10-color multiplexing and better accuracies for fewer colors. Most misclassification occurred at low signal levels, such that establishing a nonclassifiable zone near the origin of RGB color space improved the overall 10-color classification accuracy to ≥94%. Similar improvements in accuracy with greater retention of data were possible with a probabilistic rather than a radial threshold. Simulations that were parameterized by experimental data suggested that ≥14-color multiplexing with accuracies ≥90% should be possible with an optimized supra-QD color set. This study is an important foundation for advancing RGB color-based multiplexing for imaging and analyses with smartphone cameras and related charge-coupled device and CMOS color image sensor technologies.