Three-dimensional Color Space Research Articles

Shadow removal method for high-resolution aerial remote sensing images based on region group matching

Shadow removal is a critical step in preprocessing high-resolution aerial remote sensing images. The presence of shadows reduces the information in high-resolution aerial remote sensing images, lowers the image quality, and seriously interferes with the work of feature classification, object detection, and information extraction. Therefore, this paper proposes a high-resolution aerial remote sensing image shadow removal method based on region group matching. The method starts from both spatial information and color information, and is able to take into account the whole and the local, and recover the detailed features of different features inside the shadows. Firstly, this paper proposes an irregular region color transfer method based on three-dimensional color space, which can effectively restore the original color and texture information of the features in the shadow region. Secondly, considering the random and variable direction of feature distribution in remote sensing images, the direction of texture feature extraction cannot be set in advance. Therefore, in this paper, the texture details of the features are extracted based on the rotationally invariant light-independent texture feature extraction method to exclude the abnormal interference. Then, the shadow and light regions of the image are segmented internally and grouped according to the type of features to avoid subsequent matching anomalies due to the small size of the image blocks. Next, construct an average texture feature vector for each group of image blocks for grouping matching. The matched light groups are then used to guide the shadow groups for local feature information enhancement, which ensures that on top of the overall shadow removal, the detail recovery of different local features is enhanced. Finally, for the boundary effect after shadow removal, a boundary optimization method with dynamically assigned weights is proposed, which can ensure the natural coherence of the resultant image in the boundary part. The experimental results show that the proposed method balances both overall and local shadow removal effects and can effectively achieve high-resolution aerial remote sensing image shadow removal. Compared with existing methods, the proposed method achieves the best quality of shadow removal results and restores more realistic ground features in shadow region.

Non-invasive spectroscopic analyses of M. E. Chevreul’s colour plates: shedding new light on 19th century colour theory representation

During the early 19th century, colour’s theories flourished. In this context, Michel-Eugène Chevreul (1786–1889) developed a three-dimensional hemispherical colour space, reproduced and used for several decades by manufacturers, scientists, artists, architects, gardeners and designers, among others, to easily identify and classify colours. Despite the major influence and variety of chromatic spaces created by M. E. Chevreul, they have been the subject of a few scientific studies until now. This study will provide new information on the evolution of the printing technique, the materiality and the visual appearance of six different colour circles printed in 1847, 1855, 1861 and 1864. A dedicated analytical methodology was implemented combining visible and near-infrared reflectance imaging, X-ray fluorescence, Raman, short-wave infrared fibre optic reflectance and mid-infrared Fourier transform spectroscopies. This material identification revealed the coexistence of newly synthetized inorganic pigments, such as lead chromate, Prussian blue or Emerald/Scheele green, with established organic and inorganic pigments, such as cochineal lake, vermilion or minium. This study revealed a temporal evolution of the printing technique that leads to an improved colour representation.

Correction of Congenital Syndactyly of the Hand with Minimal Full-Thickness Skin Graft from the Weight-Bearing Midline Plantar Area.

Traditional skin grafts for syndactyly often cause color mismatches and unsightly donor sites, whereas no-skin-graft methods leave noticeable dorsal hand scars. This study presents plantar full-thickness skin graft (FTSG) from the weight-bearing midline area for syndactyly repair, a novel approach not previously reported in the literature. The study included three groups of patients with congenital syndactyly of the hand who underwent primary operations with plantar FTSG (n=70), groin FTSG (n=20), and no-skin-graft techniques (n=22). Postoperative outcomes were evaluated by an assessment panel, and guardians' satisfaction scores were measured. Color similarity between the graft and surrounding skin was assessed using a three-dimensional color space. The plantar FTSG group demonstrated a significantly higher likelihood of receiving an 'excellent' rating compared to the groin FTSG group, with an odds ratio of 6.30 (p<0.001). Color difference analysis showed that plantar FTSG more closely matched surrounding skin color than groin FTSG (6.33 vs. 22.57, p<0.001). Guardians reported greater satisfaction with outcomes on the hand in the plantar FTSG group compared to the groin FTSG and no-skin-graft groups (7.16 vs. 5.05 and 4.36, p<0.001). Satisfaction with donor sites was also significantly higher in the plantar FTSG group than in the groin FTSG group (8.23 vs. 6.30, p<0.001). Correction of congenital hand syndactyly using midline plantar FTSG from the weight-bearing area can reduce scarring on the hand dorsum, ensure superior color similarity with surrounding skin, and offer inconspicuous donor sites compared to no-skin-graft or groin FTSG techniques.

Two-stage image colorization via color codebook

Image colorization has a wide range of applications, but it remains a challenging task due to it is an inherently ill-posed problem with multi-modal uncertainty. The advancement of deep learning techniques has provided extensive avenues for addressing image colorization. However, current works mainly suffer from two problems: inaccurate colorization leading to biased color tones (e.g., cool or warm bias) and undersaturation of images. Existing Transformer-based methods can produce impressive results, but they often come with high training costs and may result in color overflow effects. In this paper, we propose a two-stage image colorization strategy based on a color codebook. Clustering methods in the three-dimensional CIE Lab color space is proposed to integrate brightness information so that the colors in the codebook can be lifelike. In the first stage, we treat the colorization task as a classification problem based on a color codebook, and a high-quality codebook is advantageous for enhancing color classification accuracy. In the second stage, different from the traditional Transformer-based method, a pyramid-type Transformer structure is used to extract rich image features to refine the colors, which can solve potential color bands, color errors and color overflow. In addition, the parameters and FLOPs are significantly smaller than other traditional Transformer-based methods. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches. On the ImageNet validation set, the achieved values are 4.60, 25.23, 0.19, and 39.82 in terms of FID, PSNR, LPIPS, and CF, respectively. On the COCO-Stuff validation set, the achieved values are 5.62, 25.15, 0.19, and 36.25 in terms of FID, PSNR, LPIPS, and CF, respectively. The codes are available at https://github.com/Tanghui2000/Two-stage_Image_Colorization_via_Color_Codebook.

Prolonged vase life by salicylic acid treatment and prediction of vase life using petal color senescence of cut lisianthus

This study investigated the effects of exogenous salicylic acid (SA) concentrations (0, 0.1, 0.3, or 0.5 mM), treatment timing (vegetative or reproductive period), and cultivation methods (soil or hydroponic cultivation) on the vase life of four lisianthus cultivars after flower cutting. A 0.5 mM concentration of the SA application during the reproductive period resulted in the longest average vase life of 15.3 d for the Blue Picote cultivar after flower cutting. A colorimeter and a chlorophyll meter were used to observe and quantify petal color changes after cutting, and the color changes during the senescence stages are visualized using a three-dimensional color space. The three-dimensional visualization demonstrated the color senescence for each measurement period. Based on these data, we used automated machine learning algorithms to predict the vase longevity, and the predictive model showed a good accuracy. This model is expected to be widely used in the floricultural industry.

Influence of prophylactic fluoride agents on the color changes and surface roughness of polymer and rhodium coated nickel-titanium orthodontic archwires

The effect of fluoride use on the color change and surface characteristics of coated nickel and titanium (NiTi) orthodontic archwires was investigated. Epoxy resin, PTFE or rhodium-coated archwires were exposed to acidulated phosphate fluoride (APF) or sodium fluoride (NaF) or artificial saliva (AS), simulating three-month clinical trials. Color changes (∆E) were assessed using a laboratory spectrophotometer in the three-dimensional CIELab color space. The roughness (Ra) and surface structure of the archwires were examined using a non-contact profilometer and scanning electron microscopy (SEM), respectively. Data were analyzed using two-way and one-way variance (ANOVA) and post-hoc Bonferroni test (α=0.05). The average values of ∆E and Ra were the highest for epoxy-APF archwires and the lowest for rhodium-AS archwires. A significant relationship was found between the archwires surface treatment method and the ∆E and Ra values. After three months, greater changes in color and roughness were observed with APF than with AS.

The Helmholtz legacy in color metrics: Schrödinger’s color theory

This study is a continuation of the authors’ previous work entitled “Helmholtz and the geometry of color space: gestation and development of Helmholtz’s line element” (Peruzzi and Roberti in Arch Hist Exact Sci. https://doi.org/10.1007/s00407-023-00304-2, 2023), which provides an account of the first metrically significant model of color space proposed by the German polymath Hermann von Helmholtz in 1891–1892. Helmholtz’s Riemannian line element for three-dimensional color space laid the foundation for all subsequent studies in the field of color metrics, although it was largely forgotten for almost three decades from the time of its first publication. The rediscovery of Helmholtz’s masterful work was due to one of the founders of quantum mechanics, Erwin Schrödinger. He established his color metric in three extended papers submitted in 1920 to the Annalen der Physik. Two memoirs were devoted to the so-called lower color metric, which laid the basis for the development of his higher color metric, exposed in the last paper. Schrödinger’s approach to the geometry of color space has been taken as a starting point for future elaborations of color metrics and allows a close examination of the current assumptions about the analysis of color-matching data. This paper presents an overall picture of Schrödinger’s works on color. His color theory developed a tradition first inaugurated by Newton and Young, and which acquired strong scientific ground with Grassmann’s, Maxwell’s, and Helmholtz’s contributions in the 1850s. Special focus will be given to Schrödinger’s account of color metric, which responded directly to Helmholtz’s hypothesis of a Riemannian line element for color space.

Elastica Models for Color Image Regularization

The choice of a proper regularization measure plays an important role in the field of image processing. One classical approach treats color images as two- dimensional surfaces embedded in a five-dimensional spatial-chromatic space. In this case, a natural regularization term arises as the image surface area. Choosing the chromatic coordinates as dominating over the spatial ones, we can think of the image spatial coordinates could as a parameterization of the image surface manifold in a three-dimensional color space. Minimizing the area of the image manifold leads to the Beltrami flow or mean curvature flow of the image surface in the three-dimensional color space, while minimizing the elastica of the image surface yields an additional interesting regularization. Recently, we proposed a color elastica model, which minimizes both the surface area and the elastica of the image manifold. In this paper, we propose to modify the color elastica and introduce two new models for color image regularization. The revised measures are motivated by the relations between the color elastica model, Euler’s elastica model, and the total variation model for gray level images. Compared to our previous color elastica model, the new models are direct extensions of Euler’s elastica model to color images. The proposed models are nonlinear and challenging to minimize. To overcome this difficulty, two operator-splitting methods are suggested. Specifically, nonlinearities are decoupled by the introduction of new vector- and matrix-valued variables. Then, the minimization problems are converted to initial value problems which are time-discretized by operator splitting. Each subproblem, after splitting, either has a closed-form solution or can be solved efficiently. The effectiveness and advantages of the proposed models are demonstrated by comprehensive experiments. The benefits of incorporating the elastica of the image surface as regularization terms compared to common alternatives are empirically validated.

Convolution Feature Inference-Based Semantic Understanding Method for Remote Sensing Images of Mangrove Forests

The semantic segmentation and understanding of remote sensing images applying computer technology has become an important component of monitoring mangrove forests’ ecological changes due to the rapid advancement of remote sensing technology. To improve the semantic segmentation capability of various surface features, this paper proposes a semantic understanding method for mangrove remote sensing images based on convolution feature inference. Firstly, the sample data is randomly selected, and next a model of convolution feature extraction is used to obtain the features of the selected sample data and build an initial feature set. Then, the convolution feature space and rule base are generated by establishing the three-dimensional color space distribution map for each class and domain similarity is introduced to construct the feature set and rules for reasoning. Next, a confidence reasoning method based on the convolution feature region growth, which introduces an improved similarity calculation, is put forward to obtain the first-time reasoning results. Finally, this approach adds a correction module, which removes the boundary information and reduces the noise from the results of the first-time reasoning as a new sample to correct the original feature set and rules, and uses the corrected feature set and rules for reasoning and understanding to obtain the final image segmentation results. It uses the corrected feature set and rules for reasoning and understanding to obtain the final image segmentation results. Experiments show that this algorithm has the benefits of a simple process, a short training time, and easy feature acquisition. The effect has been obviously improved compared to a single threshold segmentation method, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and other image segmentation methods.

A spectral three-dimensional color space model of tree crown health.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m2) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n = 122 NAIP state x year combinations; median overall accuracy 0.89–0.90; median Kappa 0.85–0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.

Evaluation of grinding surface roughness based on gradient similarity and color similarity

Existing machine vision detection methods for surface roughness based on design indices are mainly divided into two categories: grayscale information-based and color information-based. The former loses a large amount of image information when converting the image’s three-dimensional color space to one-dimensional grayscale space. The latter does not fully consider the color information and structural detail changes in images of different rough surfaces. To address the above problems, a visual measurement method of grinding surface roughness based on gradient similarity and color similarity is proposed in this study. This method purposefully uses gradient similarity and color similarity to evaluate the structural differences and color differences between different roughness images, respectively. The comparison experiments with CD, F2, and F5 indices show that the GC index we proposed has a strong correlation with the grinding surface roughness, and its regression fitting prediction model has a high prediction accuracy. In addition, we have discussed the effect of light intensity on the GC index. The experimental result indicates that the correlation between the GC index and roughness is relatively stable under different light intensities.

Color-speckle assessment in multi-primary laser-projection systems based on a 3D Jzazbz color space.

We propose and demonstrate a color-speckle assessment method based on a three-dimensional Jzazbz color space, which is appropriate for both three-primary and multi-primary systems. In the proposed scheme, new physical quantities are defined to describe the color-speckle characteristics, which provides a general and intuitive color-speckle evaluation for different laser projectors. Experimental verification is also performed using three-primary and six-primary laser projectors. The simulation and measurement results are consistent.

Comparison of digital photography and spectrometry for evaluating colour perception in humans and other trichromatic species

Digital photography and spectrometry are widely used for colour measurement, but both methods have a number of advantages and disadvantages. Comparative studies can help determine the most appropriate method for quantifying animal colour perception, but few have attempted to compare them based on colour model conversion. Here we compare colour measurements from digital photography and spectrometry in a controlled standard experimental environment using the three-dimensional colour space model CIE L*a*b* which is designed to approximate colour perception in humans and assess the repeatability and agreement of the two methods. For digital photography, we first extracted RGB values from each colour patch and transferred these to L*a*b* values using colour model conversion. For spectrometry, we measured the spectral reflectance (SR) value and subsequently transferred SR values to L*a*b* values. Using a consensus of correlation analysis, intraclass correlation coefficients, concordance correlation coefficients, and Bland-Altman analysis, we found that although spectrometry showed a slightly higher repeatability than photography, both methods were highly repeatable and showed a strong agreement. Furthermore, we used Bland-Altman analysis to derive the limits of agreement, which can be used as criteria for identifying when photography and spectrometry could be as a suitable alternative for measuring colour perception in humans and other trichromatic species. We suggest that our workflow offers a practical and logical approach that could improve how we currently study colour perception in trichromats. Measuring colour efficiently and accurately is necessary for investigating the evolutionary biology of colour perception in animals. Digital photography and spectrometry are two methods widely used for colour measurement, but there are benefits and limitations to using either method. Comparative studies based on colour model conversion are therefore critical for helping researchers determine which method is most appropriate. Here we test the repeatability and agreement of the two measuring methods using standard colour patches, as a comparative case study of broader interest in measuring colour perception in humans and similar primates. Our results demonstrate that both methods are highly repeatable, and the two methods may be used interchangeably to measure colour perception in humans under experimental conditions.

On Visualization of the Anisotropy of the Dispersion of Velocities of Polydisperse Dust in a Gas-Dust Medium

A large number of publications have been devoted to studying the features of the flow in two-phase flows in a gas-dispersed flow with inertial dust particles (shock waves, jets, turbulence, regular structures such as plasma crystals). In recent years, the study of the behavior of various fractions of impurity particles in polydisperse dust mixtures, expressed in the spatial separation of their distributions, has been of increasing interest. Spatial variations of individual components of the dust mixture make it possible to diagnose the state of the carrier gas phase in those cases when the carrier phase is very rarefied and cannot be observed directly (interstellar gas). This work is a continuation of the authors’ work [14], in which an original algorithm was proposed based on the use of three-dimensional color space resources, which allows visualizing spatial distributions of concentrations of up to three fractions of a polydisperse mixture of dust particles simultaneously, as well as an entropy measure that allows visualizing the degree of polydispersity in a heterogeneous gas-and-dust environment. However, the structural features of the impurity phase (caustics, areas of segregation of dust particles by their size, mass, etc.) are not necessarily spatially combined with its dynamic features (collisionless shock waves, flow turning points, stagnation points, accumulation points). In this paper, we propose a method for visualizing the dynamic features of a polydisperse collisionless mixture of particles in a two-phase gas-dust medium by constructing spatial distribution maps of the particle velocity dispersion anisotropy regions. Strong anisotropy of the velocity dispersion corresponds to the region in which an intense multi-stream flow occurs. Dust is considered as a mixture of several fractions, each of which contains particles of the same type, while the particle sizes in different fractions are different. For each dust fraction, a field is constructed of the spatial distributions of the eccentricities of the velocity dispersion ellipses. To visualize the degrees of anisotropy of the velocity dispersion of two fractions of a polydisperse dust mixture at once, a specially selected entropy measure is proposed. The results of two-dimensional demonstration calculations of a turbulent gas-dust medium and the anisotropy map in the distribution of the velocity dispersion of an impurity dust component are presented.

Determination and Measurement of Color Dissimilarity

There are millions of different colors that exist in this nature. There are colors that can easily be distinguished from other colors, but many are also difficult to distinguish. The ability to distinguish colors is important in the recognition of an object, especially objects in an image. Color can be represented in a three-dimensional RGB color space and each color will have an RGB value with a range of values ??from 0-255. Conceptually, colors with different RGB values ??are different colors, but the human eye may not necessarily be able to distinguish them. This study aims to determine and measure the color dissimilarity that can be distinguished by humans. With an experiment comparing a color with a color made with a variation of RGB values, this study resulted that two colors that have a Delta E (?E) value of 8 can be distinguished by the eye. This result is obtained if both colors have a value of ?R, ?G or ?B of at least 8, or a value of ?RG, ?RB or ?GB of at least 6, or a value of ?RGB of at least 5.

Colour change of ceramic brackets with the use of coloured beverages in adolescent patients: A randomized clinical trial

To clinically evaluate colour change of ceramic orthodontic brackets with the use of coloured beverages as ceramic brackets' resistance to discoloration has become a major concern. A prospective two parallel groups-split mouth randomized trial with a 1:1 allocation ratio. Two equal groups including 40 adolescent patients from the orthodontic department (Faculty of Dentistry, Mansoura University, Egypt), scheduled for maxillary premolar extractions were bonded with 2 types of mono-crystalline ceramic brackets: Type 1 (Inspire ICETM) and Type 2 (Radiance PlusTM). Participants in each group were asked to rinse with either black tea or Cola. After extraction, the colour of the 80 debonded brackets was compared to that of 20 control brackets from each type by spectrophotometer according to the international standard CIELAB colour space (CIE L*a*b*). The latter consists of three coordinates: L* (lightness value), a* and b* (the colour channels). The total colour difference ΔE* is the distance between two colours in this three-dimensional colour space. The colour change was also assessed by digital image analysis according to the RGB model. Type 1 brackets showed mean ΔE* values of 2.24±0.25 in black tea and 1.76±0.1 in Cola groups (P<0.001), while Type 2 brackets showed means of 1.99±0.15 in black tea and 1.56±0.1 in Cola groups (P<0.001). The mean RGB values were 174.3±12.02 in black tea and 185.6±6.9 in the Cola groups of type 1 brackets (P<0.001), while were 166.5±17.8 in black tea and 190.8±8.9 in Cola groups of type 2 brackets (P<0.001). Black tea showed more significant effect than Cola on the two bracket types. Bracket type affected the colour change in each beverage group.

Underwater Single Image Dehazing Using the Color Space Dimensionality Reduction Prior

Underwater images suffer from low visibility and contrast caused by absorption and scattering, which leads to haze and some further limitations. The existing underwater single image dehazing methods cannot achieve a balance between the performance and computational complexity, and are difficult to produce satisfactory results in the regions with large distance. To overcome these problems, we propose a new underwater single image dehazing method, which includes an improved background light estimation based on the quad-tree subdivision iteration algorithm, and a novel transmission estimation method. For the background light estimation, we introduce a robust score for each region of the image, which can evaluate the region from both smoothness and color. For the transmission estimation, we propose the color space dimensionality reduction prior (CSDRP), which allows conversing an image from the three-dimensional RGB color space to a 2D color space, namely the UV color space. In the UV color space, by clustering the pixels into mounts of haze-lines and carefully setting the haze-free boundary, the transmission map can be figured out and used to produce an excellent dehazed image. Experimental results show that our method has competitive effects compared with mainstream underwater single image dehazing methods.

A study of a clothing image segmentation method in complex conditions using a features fusion model

According to a priori knowledge in complex conditions, this paper proposes an unsupervised image segmentation algorithm to be used for clothing images that combines colour and texture features. First, block truncation encoding is used to divide the traditional three-dimensional colour space into a six-dimensional colour space so that more fine colour features can be obtained. Then, a texture feature based on the improved local binary pattern (LBP) algorithm is designed and used to describe the clothing image with the colour features. After that, according to the statistical appearance law of the object region and background information in the clothing image, a bisection method is proposed for the segmentation operation. Since the image is divided into several subimage blocks, bisection image segmentation will be accomplished more efficiently. The experimental results show that the proposed algorithm can quickly and effectively extract effective clothing regions from complex circumstances without any artificial parameters. The proposed clothing image segmentation method will play an important role in computer vision, machine learning applications, pattern recognition and intelligent systems.

The Cambridge Colour Test: Reliability of discrimination trivectors in colour space

Background: The reliable assessment of colour discrimination has become increasingly important as some ophthalmic diseases and systemic conditions manifest themselves via acquired impairments of colour vision. The Cambridge Colour Test (CCT) is a computerised procedure, developed using the basic principles of traditional pseudoisochromatic plates to evaluate colour function or discrimination. Aim: The study investigated reliability of the CCT, with emphasis on the Trivector subtest, which measures thresholds along the three protan, deutan and tritan confusion lines to probe the sensitivity of the long, medium and short wavelength cones. Setting: The study used a prospective observational and quantitative design and took place in a research and clinical environment within the Department of Optometry at the University of Johannesburg, South Africa. Participants were 20 young adults of both genders aged 19–24 years with normal colour vision. Methods: Two measurements (test and retest) of the Trivector test were measured monocularly (right eye) for all participants to assess the reliability of repeated measurements. Reliability was assessed using coefficients of repeatability, coefficients of variation and Bland–Altman plots with limits of agreement (LoA). Results: Outcomes for the Bland–Altman LoA showed good intra-individual agreement and coefficients of repeatability and reliability revealed generally high test–retest repeatability. Multivariate stereo-pair scatter plot comparisons of means and differences of test and retest measurements show that points cluster tightly within the same region in the three-dimensional colour space, and the centroids of the ellipsoids representing the mean differences are close to the origin at zero. Conclusion: Although departure from normality and outlying values can have important influences, essentially the CCT provides very similar test and retest results for the Trivector test.

Chromatic and luminance sensitivity for skin and skinlike textures.

Despite the importance of the appearance of human skin for theoretical and practical purposes, little is known about visual sensitivity to subtle skin-tone changes, and whether the human visual system is indeed optimized to discern skin-color changes that confer some evolutionary advantage. Here, we report discrimination thresholds in a three-dimensional chromatic-luminance color space for natural skin and skinlike textures, and compare these to thresholds for uniform stimuli of the same mean color. We find no evidence that discrimination performance is superior along evolutionarily relevant color directions. Instead, discriminability is primarily determined by the prevailing illumination, and discrimination ellipses are aligned with the daylight locus. More specifically, the area and orientation of discrimination ellipses are governed by the chromatic distance between the stimulus and the illumination. Since this is true for both uniform and textured stimuli, it is likely to be driven by adaptation to mean stimulus color. Natural skin texture itself does not confer any advantage for discrimination performance. Furthermore, we find that discrimination boundaries for skin, skinlike, and scrambled skin stimuli are consistently larger than those for uniform stimuli, suggesting a possible adaptation to higher order color statistics of skin. This is in line with findings by Hansen, Giesel, and Gegenfurtner (2008) for other natural stimuli (fruit and vegetables). Human observers are also more sensitive to skin-color changes under simulated daylight as opposed to fluorescent light. The reduced sensitivity is driven by a decline in sensitivity along the luminance axis, which is qualitatively consistent with predictions from a Von Kries adaptation model.