Neighboring Color Research Articles

Evaluating the Impact of Elementary School Urban Neighborhood Color on Children’s Mentalization of Emotions through Multi-Source Data

To address the challenge of quantitatively assessing the mentalization of emotions in color design schemes, this study uses Baidu Street View images and deep learning, integrates multi-source data, and innovatively constructs a color data model based on a comprehensive color indicator system for the quantitative assessment and visual representation of how the color environments of elementary school urban neighborhoods impact children’s mentalization of emotions. This model systematically incorporates physical color indicators, integrates elements such as perceptual frequency, and provides a novel perspective for color planning. The study’s results reveal that color metrics significantly impact children’s mentalization of emotions across multiple dimensions, with gender and age emerging as important influencing factors. Additionally, significant correlations were found between color and environmental elements such as building façades, roads, and signs. The study provides urban planners and architects with a practical color data model and recommendations for the revitalization of elementary school urban neighborhoods, offering a scientific basis for optimizing color design.

Counting rainbow triangles in edge‐colored graphs

AbstractLet be an edge‐colored graph on vertices. The minimum color degree of , denoted by , is defined as the minimum number of colors assigned to the edges incident to a vertex in . In 2013, Li proved that an edge‐colored graph on vertices contains a rainbow triangle if . In this paper, we obtain several estimates on the number of rainbow triangles through one given vertex in . As a consequence, we prove counting results for rainbow triangles in edge‐colored graphs. One main theorem states that the number of rainbow triangles in is at least , which is best possible by considering the rainbow ‐partite Turán graph, where its order is divisible by . This means that there are rainbow triangles in if , and rainbow triangles in if when . Both results are tight in the sense of the order of the magnitude. We also prove a counting version of a previous theorem on rainbow triangles under a color neighborhood union condition due to Broersma et al., and an asymptotically tight color degree condition forcing a colored friendship subgraph (i.e., rainbow triangles sharing a common vertex).

Graphical modeling of additive color mixing. Analyses of electromagnetic effects as colors of the vision analyzer

The human visual analyzer is a high form of evolution. Some jellyfish can distinguish light and dark objects with sensitive cells. Squids, octopuses, and nautiluses from Cephalopods family have eyes with which they can see objects. Visual information is processed in the brain. In fishes, the visual analyzer evolved. Fishes living below 100 m depth inhabit a world with blue light. The authors consider the evolution of vision in these fishes due to the lowest absorption of blue and violet colors of electromagnetic waves from the optical spectrum. Subsequently, on land, in humans vision evolved towards the green color. Green is the most prevalent color on the land. In humans, three light-sensitive cones evolved – S, M, and L. S cones are most sensitive to blue, M – to green, and L – to red color in the spectrum. Some humans retain sensitivity of S to genetic changes and disorders. Additive color mi­xing is a high form of color perception in humans. The additive mixing of different colors achieves a new color in the human visual analyzer. Remarkably, different individuals perceive observed images in varying manners. Mixing neighboring colors on the spectrum gives one, two, or three colors. This process is estimated using graphical modeling. The graphical modeling with two colors allows for the creation of additive colors. One, two, or three additive colors can be obtained. The production of colors results from the light sensitivity of S, M, and L cones. An analysis that phy­sically demonstrates the mixing of green and red colors, resulting in the colors yellow or orange. When blue and red are mixed, the resulting colors are green, yellow, and orange is proposed. The additive mixing of blue and yellow gives green. The subjective nature of visual perception, influenced by the viewer’s heightened sensitivity to one of the colors, beco­mes evident in the presence of two colors. Notably, when the background is violet, the sensitivity of the blue cones diminishes.

VINCENT: Cyber-threat detection through vision transformers and knowledge distillation

Vision Transformers (ViTs) denote a family of attention-based deep learning techniques that have recently achieved amazing results in various problems related to the field of computer vision. In this paper, we explore the use of ViTs in problems of cyber-threat detection related to malware and network intrusion detection. In particular, we propose VINCENT, that is a novel deep neural method, which resorts to a color imagery representation of cyber-data by encoding related cyber-data features into neighboring color pixels. ViTs are trained from cyber-data images as teacher models, to extract explainable imagery signatures of cyber-data classes. This knowledge is extracted by leveraging the self-attention mechanism to give paired attention values between pairs of imagery patches. The signature knowledge, extracted through the ViT teacher, is, finally, used to train a smaller neural student model according to the knowledge distillation theory. Experiments with various benchmark cybersecurity datasets assess the accuracy of the student model VINCENT also compared to that of several state-of-the-art methods. In addition, it shows that VINCENT can obtain insights from explanations recovered through the self-attention mechanism of the ViT teacher.

Properly colored and rainbow C4 ${C}_{4}$'s in edge‐colored graphs

AbstractWe present new sharp sufficient conditions for the existence of properly colored and rainbow 's in edge‐colored graphs. Our first results deal with sharp color neighborhood conditions for the existence of properly colored 's in edge‐colored complete graphs and complete bipartite graphs, respectively. Next, we characterize the extremal graphs for an anti‐Ramsey number result due to Alon on the existence of rainbow 's in edge‐colored complete graphs. We also generalize Alon's result from complete to general edge‐colored graphs. Finally, we derive a structural property regarding the extremal graphs for a bipartite counterpart of Alon's result due to Axenovich, Jiang, and Kündgen on the existence of rainbow 's in edge‐colored complete bipartite graphs. We also generalize their result from complete to general bipartite edge‐colored graphs.

On the Total Set Chromatic Number of Graphs

Given a vertex coloring c of a graph, the neighborhood color set of a vertex is defined to be the set of all of its neighbors’ colors. The coloring c is called a set coloring if any two adjacent vertices have different neighborhood color sets. The set chromatic number χs(G) of a graph G is the minimum number of colors required in a set coloring of G. In this work, we investigate a total analog of set colorings; that is, we study set colorings of the total graph of graphs. Given a graph G = (V, E), its total graph T(G) is the graph whose vertex set is V ∪ E and in which two vertices are adjacent if and only if their corresponding elements in G are adjacent or incident. First, we establish sharp bounds for the set chromatic number of the total graph of a graph. Furthermore, we study the set colorings of the total graph of different families of graphs.

Color neighborhood union conditions for proper edge-pancyclicity of edge-colored complete graphs

Let G be an edge-colored complete graph on n vertices such that there exist at least n distinct colors on edges incident to every pair of its vertices. In this paper, we first show that every edge of G with n≥6k−19 is contained in a properly colored cycle of length k. Further, we prove that if G contains no monochromatic triangles, then there exists a properly colored path of length l for every 1≤l≤n−1 between each pair of vertices of G and every vertex of G is contained in a properly colored cycle of length k for any 3≤k≤n.

The set chromatic numbers of the middle graph of graphs

For a simple connected graph G, let c : V(G) → ℕ be a vertex coloring of G, where adjacent vertices may be colored the same. The neighborhood color set of a vertex v, denoted by NC(v), is the set of colors of the neighbors of v. The coloring c is called a set coloring provided that NC(u) ≠ NC(v) for every pair of adjacent vertices u and v of G. The minimum number of colors needed for a set coloring of G is referred to as the set chromatic number of G and is denoted by χs(G). In this work, the set chromatic number of graphs is studied in relation to the graph operation called middle graph. Our results include the exact set chromatic numbers of the middle graph of cycles, paths, star graphs, double-star graphs, and some trees of height 2. Moreover, we establish the sharpness of some bounds on the set chromatic number of general graphs obtained using this operation. Finally, we develop an algorithm for constructing an optimal set coloring of the middle graph of trees of height 2 under some assumptions.

Zero forcing number of graphs with a power law degree distribution.

The zero forcing number is the minimum number of black vertices that can turn a white graph black following a single neighbor color forcing rule. The zero forcing number provides topological information about linear algebra on graphs, with applications to the controllability of quantum dynamical systems. Here, I investigate the zero forcing number of undirected graphs with a power law degree distribution p_{k}∼k^{-γ} by means of numerical simulations. For graphs generated by the preferential attachment model, with a diameter scaling logarithmically with the graph size, the zero forcing number approaches the graph size when γ→2. In contrast, for graphs generated by the deactivation model, with a diameter scaling linearly with the graph size, the zero forcing number is smaller than the graph size independently of γ. Therefore the scaling of the graph diameter with the graph size is another factor determining the controllability of dynamical systems. These results have implications for the controllability of quantum dynamics on energy landscapes, often characterized by a complex network of couplings between energy basins.

Building the Beloved Community: Philadelphia's Interracial Civil Rights Organizations and Race Relations, 1930-1970, by Stanley Keith Arnold

<i>Building the Beloved Community: Philadelphia's Interracial Civil Rights Organizations and Race Relations, 1930-1970</i>, by Stanley Keith Arnold

On sufficient conditions for rainbow cycles in edge-colored graphs

Let G be an edge-colored graph. We use e(G) and c(G) to denote the number of edges of G and the number of colors appearing on E(G), respectively. For a vertex v∈V(G), the color neighborhood of v is defined as the set of colors assigned to the edges incident to v. A subgraph of G is rainbow if all of its edges are assigned with distinct colors. The well-known Mantel’s theorem states that a graph G on n vertices contains a triangle if e(G)≥⌊n24⌋+1. Rademacher (1941) [8] showed that G contains at least ⌊n2⌋ triangles under the same condition. Li et al. (2014) proved a rainbow version of Mantel’s theorem: An edge-colored graph G has a rainbow triangle if e(G)+c(G)≥n(n+1)∕2. In this paper, we first characterize all graphs G satisfying e(G)+c(G)≥n(n+1)∕2−1 but containing no rainbow triangles. Motivated by Rademacher’s theorem, we then characterize all graphs G which satisfy e(G)+c(G)≥n(n+1)∕2 but contain only one rainbow triangle. We further obtain two results on color neighborhood conditions for the existence of rainbow short cycles. Our results improve a previous theorem due to Broersma et al. (2005). Moreover, we provide a sufficient condition in terms of color neighborhood for the existence of a specified number of vertex-disjoint rainbow cycles.

Reconstruction of missing color-channel data using a three-step back propagation neural network

Demosaicking aims to approximate missing color pixels through analysis of the geometric structure between given color pixels and missing color pixels. In this paper, we introduce an efficient adaptive demosaicking method based on back propagation (BP) neural network (BP-NN). We firstly reconstruct the green channel using one BPNN, and then refine the green channel utilizing another BPNN based on the color difference. With the whole green channel interpolated, we reconstruct the red/blue channel using the color difference between the green channel and red/blue channel in a local region. Finally, we refine the red/blue channel using the third BPNN. Regarding the interpolation issue, different image features have completely different properties, such as smooth regions, edges, and textures. Consequently, it is necessary to identify an adaptive model to estimate the relation among neighboring color pixels. We provide the adaptive BP-NN based demosaicking algorithm which can reduce blurring through recovery of missing pixels by a learning process, and also use a pre-trained fixed network to reduce computational complexity. Experimental results demonstrate that the proposed method outperforms extant approaches in PSNR, computational complexity, and visual quality.

Statistical Detection of Colors in Dermoscopic Images With a Texton-Based Estimation of Probabilities.

Color has great diagnostic significance in dermatoscopy. Several diagnosis methods are based on the colors detected within a lesion. Malignant lesions frequently show more than three colors, whereas in benign lesions, three or fewer colors are usually observed. Black, red, white, and blue-gray are found more frequently in melanomas than in benign nevi. In this paper, a method to automatically identify the colors of a lesion is presented. A color label identification problem is proposed and solved by maximizing the posterior probability of a pixel to belong to a label, given its color value and the neighborhood color values. The main contribution of this paper is the estimation of the different terms involved in the computation of this probability. Two evaluations are performed on a database of 200 dermoscopic images. The first one evaluates if all the colors detected in a lesion are indeed present in it. The second analyzes if each pixel within a lesion is assigned the correct color label. The results show that the proposed method performs correctly and outperforms other methods, with an average F-measure of 0.89, an accuracy of 0.90, and a Spearman correlation of 0.831.

Filter-based Bayer Pattern CFA Demosaicking

In this paper, we introduce an efficient demosaicking method based on an advanced nonlocal mean (NLM) filter using adaptive weight with consideration of both neighborhood similarity and patch distance. The to-be-interpolated missing color information is considered to be populated by the weight average of directional estimation in six directions: north, south, west, east, pro-diagonal, and anti-diagonal. The NLM sets adaptive weight by obtaining neighborhood similarity between a missing color pixel and the given neighbor color pixels, and similar local neighborhoods are selected to calculate the adaptive weight. The proposed advanced NLM filter is designed to assign weight as a combination of NLM and spatial location distance. The nearer a pixel to a missing pixel, the larger is its chance of having a local structure similar to that of the to-be-interpolated pixel. Considering this evidence and from the viewpoint of reducing computational complexity, we employ location distance to control weights. The new advanced NLM filter is better parametrized, and the weights therein are impacted both by patch similarity and location distance. We present experimental results in terms of CPSNR, S-CIELAB $$\Delta $$Δ E*, and FSIM to clarify the performance of the proposed algorithm objectively and subjectively. The experimental results indicate that the proposed method outperforms existing approaches both objectively and subjectively.

Color appearance modeling of bicolor striped woven fabrics considering neighboring color effects

AbstractIn this article, the effect of the spatial and colorimetric attributes of neighboring color on color appearance shift in bicolor striped woven fabrics is investigated. A total of 240 test/neighboring woven color combinations were constructed in four different striped paradigms. Each test color in the combinations was visually assessed by 12 observer panels with the use of the magnitude estimation method estimating the magnitude of perceptual color attributes lightness, colorfulness, and hue. The visual estimates obtained were analyzed statistically by employing correlation and simple regression methods, and, as a result, the following significant neighboring color effects were detected and individually defined: (1) neighboring color's size, lightness, colorfulness, and hue on test color's lightness, (2) neighboring color's colorfulness and hue on test color's colorfulness, and (3) neighboring color's hue on test color's hue. Furthermore, through multiple regression analysis, color appearance models by which the lightness, colorfulness, and hue of bicolor woven fabrics can be predicted were derived. The predictive performance of the models was evaluated by calculating the difference between the visually estimated and the predicted color appearances, using ΔL*, ΔC*, Δh°, and ΔECMC(2:1). Among all the derived models, the model producing the smallest mean error was chosen as a final model, and its great accuracy in color appearance predictions was verified through further statistical evaluation. It is envisaged that the findings of this research are of benefit to design textile products with bicolor striped woven fabrics to have desired color appearances. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 512–521, 2017

Use of spectroscopy for assessment of color discrimination in animal vision

Animals use color vision for a number of tasks including food localization, object recognition, communication, and mate selection. For these and other specific behaviors involving the use of color cues, models that quantify color discriminability have been developed. These models take as input the photoreceptor spectral sensitivities of the animal and radiance spectra of the surfaces of interest. These spectra are usually acquired using spectroscopic instruments that collect point-by-point data and can easily yield signals contaminated with neighboring colors if not operated carefully. In this paper, I present an equation that relates the optical fiber diameter and numerical aperture to the measurement angle and distance needed to record uncontaminated spectra. I demonstrate its utility by testing the discriminability of two solid colors for the visual systems of a dichromatic ferret and a trichromatic frog in (1) a conspicuous scenario where the colors have little spectral overlap and (2) a perfect camouflage scenario where the spectra are identical. This equation is derived from geometrical optics and is applicable to spectroscopic measurements in all fields.

Hate thy neighbor: move-in violence and the persistence of racial segregation in American housing

Acknowledgments Introduction: Violence and the Neighborhood Color Line 1. The Roots of Contemporary Move-In Violence 2. The Contemporary Dynamics of Move-In Violence 3. Anti-Integrationist Violence and the Tolerance-Violence Paradox4. Racism or Power? Explaining Perpetrator Motivation in Interethnic Cases5. When Class Trumps Race: Explaining Perpetrator Motivation in Interclass Cases6. Responding to Neighborhood Hate Crimes Conclusion: The Reality of Anti-Integrationist Violence and Prospects for IntegrationNotes Index About the Author

An optimized video system for augmented reality in endodontics: a feasibility study

We propose an augmented reality system for the reliable detection of root canals in video sequences based on a k-nearest neighbor color classification and introduce a simple geometric criterion for teeth. The new software was implemented using C++, Qt, and the image processing library OpenCV. Teeth are detected in video images to restrict the segmentation of the root canal orifices by using a k-nearest neighbor algorithm. The location of the root canal orifices were determined using Euclidean distance-based image segmentation. A set of 126 human teeth with known and verified locations of the root canal orifices was used for evaluation. The software detects root canals orifices for automatic classification of the teeth in video images and stores location and size of the found structures. Overall 287 of 305 root canals were correctly detected. The overall sensitivity was about 94 %. Classification accuracy for molars ranged from 65.0 to 81.2 % and from 85.7 to 96.7 % for premolars. The realized software shows that observations made in anatomical studies can be exploited to automate real-time detection of root canal orifices and tooth classification with a software system. Automatic storage of location, size, and orientation of the found structures with this software can be used for future anatomical studies. Thus, statistical tables with canal locations will be derived, which can improve anatomical knowledge of the teeth to alleviate root canal detection in the future. For this purpose the software is freely available at: http://www.dental-imaging.zahnmedizin.uni-mainz.de/.

A novel content based image retrieval system using K-means/KNN with feature extraction

Image retrieval has been popular for several years. There are different system designs for content based image retrieval (CBIR) system. This paper propose a novel system architecture for CBIR system which combines techniques include content-based image and color analysis, as well as data mining techniques. To our best knowledge, this is the first time to propose segmentation and grid module, feature extraction module, K-means and k-nearest neighbor clustering algorithms and bring in the neighborhood module to build the CBIR system. Concept of neighborhood color analysis module which also recognizes the side of every grids of image is first contributed in this paper. The results show the CBIR systems performs well in the training and it also indicates there contains many interested issue to be optimized in the query stage of image retrieval.

Set colorings in perfect graphs

For a nontrivial connected graph $G$, let $c\colon V(G)\rightarrow \mathbb {N}$ be a vertex coloring of $G$ where adjacent vertices may be colored the same. For a vertex $v \in V(G)$, the neighborhood color set $\mathop {\rm NC}(v)$ is the set of colors of the neighbors of $v$. The coloring $c$ is called a set coloring if $\mathop {\rm NC}(u)\neq \mathop {\rm NC}(v)$ for every pair $u, v$ of adjacent vertices of $G$. The minimum number of colors required of such a coloring is called the set chromatic number $\chi _{\rm s}(G)$. We show that the decision variant of determining $\chi _{\rm s}(G)$ is NP-complete in the general case, and show that $\chi _{\rm s}(G)$ can be efficiently calculated when $G$ is a threshold graph. We study the difference $\chi (G)-\chi _{\rm s}(G)$, presenting new bounds that are sharp for all graphs $G$ satisfying $\chi (G)=\omega (G)$. We finally present results of the Nordhaus-Gaddum type, giving sharp bounds on the sum and product of $\chi _{\rm s}(G)$ and $\chi _{\rm s}({\overline G})$.