Hue Distribution Research Articles

Ensemble percepts of colored targets among distractors are influenced by hue similarity, not categorical identity.

Color can be used to group similar elements, and ensemble percepts of color can be formed for such groups. In real-life settings, however, elements of similar color are often spatially interspersed among other elements and seen against a background. Forming an ensemble percept of these elements would require the segmentation of the correct color signals for integration. Can the human visual system do this? We examined whether observers can extract the ensemble mean hue from a target hue distribution among distractors and whether a color category boundary between target and distractor hues facilitates ensemble hue formation. Observers were able to selectively judge the target ensemble mean hue, but the presence of distractor hues added noise to the ensemble estimates and caused perceptual biases. The more similar the distractor hues were to the target hues, the noisier the estimates became, possibly reflecting incomplete or inaccurate segmentation of the two hue ensembles. Asymmetries between nominally equidistant distractors and substantial individual variability, however, point to additional factors beyond simple mixing of target and distractor distributions. Finally, we found no evidence for categorical facilitation in selective ensemble hue formation.

78‐1: Invited Paper: Gamut Rings Color Scope for Use with Wide Gamut Display Systems

Traditional chromaticity scopes and vectorscopes have limited ability to visualize color appearance attributes, which limits color grading and color correction. A new tool based on gamut rings can monitor the lightness, chroma, and hue distributions of the pixel colors of input images in a single 2D diagram. The perceptually intuitive visualization is expected to facilitate more efficient and effective color grading, thereby broadening the market's understanding of the benefits of wide gamut displays.

성장소설 표지디자인에 나타난 시각적 의미패턴 연구

As Korea grew after liberation, social and cultural changes occurred and were reflected in the content and cover design of coming-of-age novels. In a situation where Korea's bildungsroman is also changing and developing, we found that research on cover design for this was very insufficient, and attempted to study the characteristic semantic system of the cover design of Korean bildungsroman by era. The research method targets the cover designs of 166 coming-of-age novels published from the 1970s to 2023. First, the meaning of keywords and titles by era of the content of the coming-of-age novels is analyzed, and second, the meaning of the cover design of the coming-of-age novels is visualized. Analyze it by dividing it into images, colors, and typography, and check the flow and patterns of change by era. Third, we analyzed the relationship between keywords and visual elements by era in the content of coming-of-age novels. Looking at the analysis results, firstly, the composition of the parts of speech in the title by era appeared to be diversifying and becoming more complex, from 1 type of ‘noun’ structure in the 1970s to 17 types of parts of speech in the 2020s. Second, in terms of images, the oil painting technique of illustrations was used the most, and as the 2000s progressed, the frequency of using the entire cover as an image increased, and the use of stripes also gradually expanded. In terms of color, in the 1970s the hue distribution was medium brightness and low saturation, in the 1990s it was a hue distribution of wide brightness and saturation with warm colors and neutral colors, and in the 2020s the hue distribution was again centered on neutral colors and cool colors with medium brightness and low saturation. appear. In terms of typography, horizontal writing, center alignment, single line, serif, asymmetrical layout, and color changes were used to emphasize headings. Third, the concreteness and abstractness of titles and images were analyzed. In the 1970s, abstract expressions were very high, and concrete expressions were used more in the 2000s. In terms of visualization of the title, the average matching rate was 63%, and it was analyzed that the specificity of the title affects image visualization. In general, keyword visualization was relatively high in the 1970s, but the matching rate gradually decreased thereafter.

BRDF representation in response to the build orientation in 3D-printed digital materials

Additively manufactured (AM) parts still lack a thorough understanding of their optical properties, particularly surface texture and reflectance characteristics at different viewing angles. This study examines the reflectance properties of material jetting (MJT) parts using bidirectional reflectance distribution functions (BRDFs). The visual appearance of the MJT parts was analyzed using a gonio-spectrophotometer at 328 unique incidence and viewing geometries for seven different wedge angles for build orientation (BO) from 0° to 90° at 15° intervals. The redundancy analysis (RDA) and principal component analysis (PCA) were used to study BO and to determine the prominent measurement geometries. The results indicate higher BOs resulted in more color and texture variation and rougher surfaces, with Sq 4.21 μm vertical compared to 1.42 μm for horizontal BOs. Furthermore, it affected the visual representation and parametric estimation of BRDF, where significantly lower luminance, more diffuse reflection, and less hue distribution were observed for all CMYK resins printed at higher BOs. Accordingly, vertically printed surfaces showed a wider near-to-specular luminance area than other BOs. An analysis of the bidirectional reflectance property suggests that a gonio-spectrophotometer can be embedded in the printing process and quality assurance in AM as a computationally efficient model.

Entropy Measurements for Leukocytes' Surrounding Informativeness Evaluation for Acute Lymphoblastic Leukemia Classification.

The study of leukemia classification using deep learning techniques has been conducted by multiple research teams worldwide. Although deep convolutional neural networks achieved high quality of sick vs. healthy patient discrimination, their inherent lack of human interpretability of the decision-making process hinders the adoption of deep learning techniques in medicine. Research involving deep learning proved that distinguishing between healthy and sick patients using microscopic images of lymphocytes is possible. However, it could not provide information on the intermediate steps in the diagnosis process. As a result, despite numerous examinations, it is still unclear whether the lymphocyte is the only object in the microscopic picture containing leukemia-related information or if the leukocyte's surroundings also contain the desired information. In this work, entropy measures and machine learning models were applied to study the informativeness of both whole images and lymphocytes' surroundings alone for Leukemia classification. This work aims to provide human-interpretable features marking the probability of sickness occurrence. The research stated that the hue distribution of images with lymphocytes obfuscated alone is informative enough to facilitate 93.0% accuracy in healthy vs. sick classification. The research was conducted on the ALL-IDB2 dataset.

Histological image color normalization using a skewed normal distribution mixed model.

Color variation between histological images may influence the performance of computer-aided histological image analysis. Therefore, among the most essential and challenging tasks in histological image analysis are the reduction of the color variation between images and the preservation of the histological information contained in the images. In recent years, many methods have been introduced with respect to the color normalization of histological images. In this study, we introduce a new clustering method referred to as the skewed normal distribution mixed model clustering algorithm. Realizing that the color distribution of hue values approximates the combination of several skewed normal distributions, we propose to use the skewed normal distribution mixture model to analyze the hue distribution. The proposed skewed normal distribution mixture model clustering algorithm includes saturation-weighted hue histograms because it takes into account the saturation and hue information of a particular histogram image, which can diminish the influence of achromatic pixels. Finally, we conducted extensive experiments based on three data sets and compared them with commonly used color normalization methods. The experiments show that the proposed algorithm has better performance in stain separation and color normalization compared to other methods.

A Preliminary Assessment of a Newly-Defined Multispectral Hue Space for Retrieving River Depth with Optical Imagery and In Situ Calibration Data

Bathymetry is a key element in the modeling of river systems for flood mapping, geomorphology, or stream habitat characterization. Standard practices rely on the interpolation of in situ depth measurements obtained with differential GPS or total station surveys, while more advanced techniques involve bathymetric LiDAR or acoustic soundings. However, these high-resolution active techniques are not so easily applied over large areas. Alternative methods using passive optical imagery present an interesting trade-off: they rely on the fact that wavelengths composing solar radiation are not attenuated at the same rates in water. Under certain assumptions, the logarithm of the ratio of radiances in two spectral bands is linearly correlated with depth. In this study, we go beyond these ratio methods in defining a multispectral hue that retains all spectral information. Given n coregistered bands, this spectral invariant lies on the (n−2)-sphere embedded in Rn−1, denoted Sn−2 and tagged ‘hue hypersphere’. It can be seen as a generalization of the RGB ‘color wheel’ (S1) in higher dimensions. We use this mapping to identify a hue-depth relation in a 35 km reach of the Garonne River, using high resolution (0.50 m) airborne imagery in four bands and data from 120 surveyed cross-sections. The distribution of multispectral hue over river pixels is modeled as a mixture of two components: one component represents the distribution of substrate hue, while the other represents the distribution of ‘deep water’ hue; parameters are fitted such that membership probability for the ‘deep’ component correlates with depth.

Color Point Cloud Registration Algorithm Based on Hue

ICP is a well-known method for point cloud registration but it only uses geometric information to do this, which will result in bad results in some similar structures. Adding color information when registering will improve the performance. However, color information of point cloud, such as gray, varies differently under different lighting conditions. Using gray as the color information to register can cause large errors and even wrong results. To solve this problem, we propose a color point cloud registration algorithm based on hue, which has good robustness at different lighting conditions. We extract the hue component according to the color information of point clouds and make the hue distribution of the tangent plane continuous. The error function consists of color and geometric error of two point clouds under the current transformation. We optimize the error function using the Gauss–Newton method. If the value of the error function is less than the preset threshold or the maximum number of iterations is reached, the current transformation relationship is required. We use RGB-D Scenes V2 dataset to evaluate our algorithm and the results show that the average recall of our algorithms is 8.63% higher than that of some excellent algorithms, and its RMSE of 14.3% is lower than that of the other compared algorithms.

Consistent image processing based on co‐saliency

In a group of images, the recurrent foreground objects are considered as the key objects in the group of images. In co-saliency detection, these are described as common saliency objects. The aim is to be able to naturally guide the user's gaze to these common salient objects. By guiding the user's gaze, users can easily find these common saliency objects without interference from other information. Therefore, a method is proposed for reducing user visual attention based on co-saliency detection. Through the co-saliency detection algorithm and matting algorithm for image preprocessing, the exact position of non-common saliency objects (called Region of Interest here, i.e. ROI) in the image group can be obtained. In the attention retargeting algorithm, the internal features of the image to adjust the saliency of the ROI areas are considered. In the HSI colour space, the three components H, S, and I are adjusted separately. First, the hue distribution is constructed by the Dirac kernel function, and then the most similar hue distribution to the surrounding environment is selected as the best hue distribution of ROI areas. The S and I components can be set as the contrast difference between ROI areas and surrounding background areas according to the user's demands. Experimental results show that this method effectively reduces the ROI areas' attraction to the user's visual attention. Moreover, comparing this method with other methods, the saliency adjustment effect achieved is much better, and the processed image is more natural.

Haze Scene Detection based on Hue, Saturation, and Dark Channel Distributions

Dehazing significantly improves image quality by restoring the loss of contrast and color saturation for images taken in the presence. However, when applied to images not taken according to the prior information, dehazing can cause unintended degradation of image quality. To avoid unintended degradations, we present a hazy scene detection algorithm using a single image based on the distributions of hue, saturation, and dark channel. Through a heuristic approach, we find out statistical characteristics of the distribution of hue, saturation, and dark channels in the hazy scene and make a detection model using them. The proposed method can precede the dehazing to prevent unintended degradation. The detection performance evaluated with a set of test images shows a high hit rate with a low false alarm ratio. Ultimately the proposed method can be used to control the effect of dehazing so that the dehazing can be applied to wide variety of images without unintended degradation of image quality.

Color ensembles: Sampling and averaging spatial hue distributions

Color serves both to segment a scene into objects and background and to identify objects. Although objects and surfaces usually contain multiple colors, humans can readily extract a representative color description, for instance, that tomatoes are red and bananas yellow. The study of color discrimination and identification has a long history, yet we know little about the formation of summary representations of multicolored stimuli. Here, we characterize the human ability to integrate hue information over space for simple color stimuli varying in the amount of information, stimulus size, and spatial configuration of stimulus elements. We show that humans are efficient at integrating hue information over space beyond what has been shown before for color stimuli. Integration depends only on the amount of information in the display and not on spatial factors such as element size or spatial configuration in the range measured. Finally, we find that observers spontaneously prefer a simple averaging strategy even with skewed color distributions. These results shed light on how human observers form summary representations of color and make a link between the perception of polychromatic surfaces and the broader literature of ensemble perception.

Face tracking with camshift algorithm for detecting student movement in a class

Face detection (face tracking) has been widely applied for various purposes, including in the fields of entertainment, education and security. Face detection can certainly be done with the camera in real time. For example on a camera on a laptop or camera in a room in real time will detect facial movements. Face detection is implemented using the camshift algorithm. The camshift algorithm works on a search window that can find facial movements in each frame. The camshift algorithm that has been applied can calculate the size and location of the search window that will be used for the next frame. The camshift algorithm can be used for detection such as face detection. The distribution used is hue in the HSV color dimension (Hue, Saturation, Value). The use of this hue distribution is done to overcome differences in human skin color and the background used when taking frames.

Rainbow schlieren-based direct visualization of thermal gradients around single vapor bubble during nucleate boiling phenomena of water

Real time non-intrusive diagnostics of thermal gradients around a single vapor bubble in isolated nucleate pool boiling with water as the working fluid have been presented. Direct visualization of transient evolution of temperature gradients in the vicinity of the heated substrate and around the single bubble as it undergoes various sub-processes has been performed using rainbow schlieren deflectometry. Boiling experiments have been conducted under saturated conditions. Results have been presented in the form of two-dimensional rainbow schlieren images wherein the degree of color re-distribution gives a direct measure of the strength of thermal gradients. Through the real time experimental images, various sub-processes/phenomena associated with boiling heat transfer such as development of superheat layer in the vicinity of the heated substrate, inception and further growth of the vapor bubble followed by scavenging of the superheat layer as the vapor bubble departs into the bulk fluid have been discussed. The profiles of hue distribution near the triple contact line brought out the presence of near stagnant fluid zone in which the heat transfer phenomenon was seen to be primarily diffusion-dominated. Beyond this narrow region, significant bulk fluid movement was observed on the basis of the spatial distributions of hue values recorded in the form of real time schlieren images. The experiments further revealed an instantaneous localized bending of the superheat layer as the bubble leaves the heater surface and subsequent shedding of the wake vortices from the edges of the departing bubble as it moves upwards in the bulk fluid.

Three-dimensional shape measurement for thin objects based on hue-height mapping using color-encoded fringe projection

Three-dimensional (3D) shape measurement based on hue-height mapping using color-encoded fringe projection is proposed for thin objects. The projected color-encoded fringe pattern is encoded by three sinusoidal fringe patterns with 2π/3 shift phase in between into red (R), green (G) and blue (B) channels, separately. It is found that the hue component is of periodicity with the same period as the sinusoidal fringe pattern in the R channel of the color-encoded fringe and the hue distribution in a period is of monotonicity. While this color-encoded fringe pattern is projected onto an object, as long as the deformation of the captured deformed color fringe pattern is not out of a period, the 3D shape of the measured object can be reconstructed directly by hue-height mapping. This method is concise and fast with no color calibration to remove color crosstalk and no time-consuming phase extraction or phase unwrapping. The experimental results show the feasibility and the practicability of the proposed method.

JCLMM: A finite mixture model for clustering of circular-linear data and its application to psoriatic plaque segmentation

The hue and chroma components of an image pixel carry crucial information that can be exploited to perform segmentation. However, due to its directional property, a circular distribution is required to characterize the hue component. In this article, we propose a mixture of bi-variate circular–linear distributions, for modelling hue and chroma information. The proposed model incorporates a joint distribution of a circular and a linear variable by means of circular copula and offers a flexible architecture that deals with heterogeneous margins for different mixture components. We apply this model for psoriatic plaque segmentation in skin images, using the hue and the chroma information. We observe that the chroma exhibits a heterogeneous distribution in a skin image. Moreover, the joint distribution of hue and chroma possesses multi-modal characteristics. Our model is suitable to perform segmentation under such circumstances. After segmentation, we perform automatic plaque localization by means of a statistical model that exploits hue information of the segmented regions. We conduct the experiments on a set of 75 psoriasis skin images. Both segmentation and localization performances are evaluated with respect to a number of commonly used criteria. The experimental results show that the proposed segmentation model outperforms several competing supervised and unsupervised methods in detecting psoriatic plaque regions in skin images.

Lightness, chroma, and hue distributions in natural teeth measured by a spectrophotometer.

Objective:The aim of the study was to analyze the distribution of color parameters, lightness (L*), chroma (C), hue (H), a* and b*, in the intercanine sector in maxilla.Material and Methods:Patients' tooth color measurements were performed using an intraoral spectrophotometer VITA Easyshade® (VITA Zahnfabrik H. Rauter GmbH and Co. KG, Bad Sackingen, Germany). The measurements were made in 255 subjects in the intercanine sector in maxilla.Results:The mean values for the group of 255 subjects were as follows: L*, a*, b*, C, and H as 81.6, 0.67, 21.6, 21.7, and 92.7, respectively. For F=206.27 and P < 0.001 between L*, a*, b*, C, H, and central incisor/lateral incisor/canines, there were statistically significant differences.Conclusion:With the statistical analysis, it was determined that there are significant color differences between the teeth of the intercanine sector, which differences are clinically significant also.

Attention-aware color theme extraction for fabric images

Color configuration plays an important role in art, design, and communication, which can influence the user’s experiences, feelings, and psychological well-being. It is laborious to manually select a color theme from scratch for handling large batches of images. Alternatively, it can inspire designers’ creations and save their time as well by leveraging the color themes in existing art works (e.g. fabric, paintings). However, it is challenging to automatically extract perceptually plausible color themes from fabric images. This paper presents a new automatic framework for extracting color themes from fabric images. A saliency map is built to help recognize the visual attention regions of the input image. Since the saliency map separates the image into visual attention regions (foreground) and non-visual attention regions (background), we respectively compute the dominant colors of these two regions, and merge them to form the initial target color theme based on certain rules. The dominant colors are extracted, accounting for the characteristics of the fabric and hue distributions of fabric images so as to acquire visually plausible results. Our method can be used to transfer colors between two fabric images for fabric color design. We tested our method thoroughly with various fabric images (e.g. cotton, silk, and linen) with different texture patterns (e.g. plain and twill). Experiments show that our method is more efficient and can generate more visually plausible results than state-of-the-art algorithms.

국내 5성급호텔 실내공간의 색채 및 색채이미지 분석 연구

The objective of this study is to examine difference in spatial color planning of lobby, restaurant, and guest rooms of domestic hotels, and analyze spatial color images to provide information on color planning for hotels that are newly built or remodeled. For this purpose, we surveyed 88 five-star hotels in Korea, collected images of their lobbies, restaurants, and guest rooms, and identified dominant colors, secondary colors, and accent colors. Based on this, we analyzed color distribution of hotel spaces, identified their color image scales according to distribution of hue and tone, and analyzed statistical significance in spatial color distributions and color images. The results are as follow. First, regarding color distribution, both dominant colors and secondary colors showed a similar pattern of distribution for all examined areas of lobby, restaurant, and guest rooms. Specifically, dominant colors were biased toward YR Y hues and pale tones, and secondary colors toward YR hues and dark tones. Meanwhile, accent colors varied by hotel areas: GY hues and dark tones were used in the lobby, YR hues and dark tones in restaurants, and Y hues and pale tones in guest rooms. Second, spatial color planning varied at a statistically significant level. In the lobby, Y hues, YR hues, and GY hues were used as dominant color, secondary color, and accent color, respectively. And dominant color was brighter than secondary or accent color, and secondary color and accent color had a higher chroma compared to dominant color. In the restaurant, YR hues were used as both dominant and secondary colors, R colors were used as accent color, and dominant color and accent color were brighter than secondary color. In the guest rooms, Y hues, YR hues, and R hues were used as dominant color, secondary color, and accent color, respectively. And dominant color was brighter than secondary or accent color, and secondary color and accent color had a higher chroma compared to dominant color. Third, as for spatial color image scales of the hotels, elegant and natural images were dominant in all examined areas of lobby, restaurant, and guest rooms. Modern images dominated the lobby and restaurant, and modest images dominated guest rooms. Fourth, statistically significant difference was observed in spatial color image scales of the hotels. Particularly, in the restaurant, modest and subtle images were relatively less common than in other areas, and guest rooms had relatively more modest, splendid or subtle images and relatively less natural and modern images compared to other areas. Fifth, most color image scales were distributed in a hard-static or a soft-static area. From these findings, we extracted a color palette for different hotel areas.

Algorithm for automatic image dodging of unmanned aerial vehicle images using two-dimensional radiometric spatial attributes

Unmanned aerial vehicle (UAV) remote sensing technology has come into wide use in recent years. The poor stability of the UAV platform, however, produces more inconsistencies in hue and illumination among UAV images than other more stable platforms. Image dodging is a process used to reduce these inconsistencies caused by different imaging conditions. We propose an algorithm for automatic image dodging of UAV images using two-dimensional radiometric spatial attributes. We use object-level image smoothing to smooth foreground objects in images and acquire an overall reference background image by relative radiometric correction. We apply the Contourlet transform to separate high- and low-frequency sections for every single image, and replace the low-frequency section with the low-frequency section extracted from the corresponding region in the overall reference background image. We apply the inverse Contourlet transform to reconstruct the final dodged images. In this process, a single image must be split into reasonable block sizes with overlaps due to large pixel size. Experimental mosaic results show that our proposed method reduces the uneven distribution of hue and illumination. Moreover, it effectively eliminates dark-bright interstrip effects caused by shadows and vignetting in UAV images while maximally protecting image texture information.

Rainbow schlieren deflectometry technique for nanofluid-based heat transfer measurements under natural convection regime

The present work reports the development and application of a lens-based rainbow schlieren deflectometry technique for non-intrusive investigation of heat transfer characteristics of dilute nanofluids. The experiments have been performed under natural convection regime. Buoyancy-driven convection has been set up in the vicinity of a heated vertical flat plate that is immersed in a quiescent ambient fluid medium with de-ionized water as the base fluid and Al2O3/water-based dilute nanofluids. The experiments have been carried out for three different volumetric concentrations of alumina nanoparticles i.e. 0.01%, 0.02% and 0.03%. The color variant of the classical schlieren technique namely the rainbow schlieren deflectometry has been employed to record the projection data of the temperature gradient field in the test cavity. The optical configuration of the imaging technique employs a specially-designed color filter for generating the desired schlieren effects. The recorded schlieren images are characterized by a gradation of hue distribution that is proportional to the strength of the temperature gradients prevailing in the test section. Results have been discussed in the form of two-dimensional rainbow schlieren images, whole field distribution of temperature gradients in the vicinity of the heated plate and local distribution of heat transfer coefficients as a function of the length of the plate for the varying concentrations of nanofluids. The profiles of local heat transfer coefficients along the length of the heated plate as obtained with nanofluids show a considerable difference when compared with that of the de-ionized water. Significant enhancement in the average values of heat transfer rates is achieved with nanofluids as the ambient fluid medium in comparison with the base fluid. The study clearly reveals the potential of rainbow schlieren deflectometry technique in the context of heat transfer measurements with nanofluids as the working medium.