Pseudo-color visualization of infrared temperature data based on color difference and HSI

Previous studies have identified differences in sensitivity characteristics between color discrimination and perception of suprathreshold color differences. However, it remains highly unclear how color difference sensitivity changes with increasing magnitudes of color difference along various color hues. This study aimed to quantify the sensitivity transition across various magnitudes of color differences and uncover the underlying mechanisms. Color discrimination sensitivities were measured using an adaptive staircase method for 32 isoluminant pedestal colors in the u'v' chromaticity diagram. For suprathreshold color differences, we employed the Maximum Likelihood Difference Scaling (MLDS) method to measure sensitivity to various color difference levels for the same 32 colors. Our findings confirmed the differences in sensitivity characteristics between discrimination and suprathreshold color difference perception. Furthermore, we observed increased sensitivities at many color category boundaries in suprathreshold color difference perception. By investigating the relation between the category effects and the color difference size levels through a model simulation, our findings suggest that the influence of color categories on the perception of color differences may become more pronounced as the magnitude of color differences increases.

A series of experiments and data analyses has been performed to test the consistency between computed and perceptual color differences in images. Five International Organization for Standardization (ISO) standard color image data (SCID) images, N2, N3, N4, N5 and N7, were tested in the experiments, whose colors were altered in CIELAB lightness, chroma, and hue, either independently or simultaneously, to form the test image pairs. CIELAB, CIE94, CIEDE2000, and CMC color differences were computed by averaging the color differences pixel by pixel for the digital images or by averaging the color differences of 256 typical color patches extracted from each image for the printed images. The digital test images were displayed on an EIZO CG19 LCD monitor and the printed test images were viewed in a D50 light booth. The experimental results showed that the lightness, chroma, and hue differences behaved differently when the perceptual color differences were plotted against the computed differences. This implied that the color-difference formulas should be optimized and that different weighting factors should be applied to different visual attributes. The color-difference formulas can be optimized from the experimental data by the slope ratio of best-fit lines of lightness, chroma, and hue. The optimized formulas CIELAB(1.5:1), CIEDE2000(2.3:1), CIE94(3.0:1), and CMC(3.4:1) for digital images, or formulas CIELAB(2.4:1.5:1), CIEDE2000(2.8:1.6:1), CIE94(2.9:1.6:1), and CMC(2.7:1.5:1) for printed images, when considering hue, performed much better than the original formulas.

Person re-identification is to match pedestrian targets observed from different camera views of multi-camera surveillance systems. Aiming at the issues that the color similarity pedestrian affect the recognition result, a person re-identification method based on multi-feature fusion in perceptual uniform color space is proposed, which is according to the characteristics of human vision system. Firstly, the color space which is suitable for similar pedestrian recognition is selected from five color spaces CIELAB, S-CIELAB, IPT, LAB2000HL and CAM02-SCD. Secondly, three kinds of pedestrian appearance features are extracted including spatial weighted histogram, local color and shape feature and global texture feature. Different distance measure methods are used to calculate the similarity of three different features. Finally, the linear fusion is performed by the adaptive weights. The experimental results based on VIPeR database and ETHZ database show that the proposed method is effective and can be used for the recognition of pedestrians with visual similarity.

Influence of background color on color perception in dentistry

Various color difference metrics were developed for characterizing the perceived color difference between individual color patches. Color difference between palettes containing multiple color patches, however, is critically important in product design and computer graphics. This study aimed to investigate how the perceived color difference between a pair of color palettes containing more than a single color patch is affected by the order and number of color patches in the palette. Two reference color sets were generated and each set had four color palettes containing 1, 4, 9, and 16 color patches that were arranged as 1 × 1, 2 × 2, 3 × 3, and 4 × 4 patterns. Human observers scaled the color differences between a color palette of the reference set and a color palette that had revised colors, or revised orders, or a combination of revised colors and orders compared to the reference palette. The calculated color differences between the two palettes were derived using the Minimum Color Difference Model (MICDM) algorithm proposed in a recent work with different color difference metrics, including CIELAB, CMC, CIE94, and DE2000. It was found that the perceived color differences of pairs of individual color patches were significantly larger than those containing multiple patches, when the calculated color differences were the same. The color differences metrics, except for CIE94, had similar performance when characterizing perceived color differences between color palettes.

Underwater image enhancement via integrated RGB and LAB color models

Traditional pseudo color coding for gray images based on image enhancement technique cannot adequately deal with some of the details information of the image. In this paper, an enhanced approach of peudo-color coding with phase-modulated image density is presented. This method has distinct levels, richer colours, and is adequate for human perception of color ,providing a better algorithm for pseudo color coding based on gray images. This method has great potential in research and application and characteristic of strong universal usage, which can process high gray resolution image.

We present an algorithm that produces a fused false color representation of a combined multiband IR and visual imaging system for maritime applications. Multispectral IR imaging techniques are increasingly deployed in maritime operations, to detect floating mines or to find small dinghies and swimmers during search and rescue operations. However, maritime backgrounds usually contain a large amount of clutter that severely hampers the detection of small targets. Our new algorithm deploys the correlation between the target signatures in two different IR frequency bands (3-5 and 8-12 im) to construct a fused IR image with a reduced amount of clutter. The fused IR image is then combined with a visual image in a false color RGB representation for display to a human operator. The algorithm works as follows. First, both individual IR bands are filtered with a morphological opening top-hat transform to extract small details. Second, a common image is extracted from the two filtered IR bands, and assigned to the red channel of an RGB image. Regions of interest that appear in both IR bands remain in this common image, while most uncorrelated noise details are filtered out. Third, the visual band is assigned to the green channel and, after multiplication with a constant (typically 1.6) also to the blue channel. Fourth, the brightness and colors of this intermediate false color image are renormalized by adjusting its first order statistics to those of a representative reference scene. The result of these four steps is a fused color image, with naturalistic colors (bluish sky and grayish water), in which small targets are clearly visible. Keywords: target detection; false color fusion, small targets, clutter, image fusion

Computer imaging of electroluminescence (EL) has been successfully applied to solar cell inspection in recent years, as EL image intensities reflect the efficiency levels and/or defects in sc-Si and mc-Si solar cells. In this paper, we propose a novel computational scheme for pseudo colorization of EL images to highlight defect regions in solar cells for human inspection. Specifically, given a template EL image and pseudo color labels on its defect regions, we impose the pseudo colors to other grayscale EL images, with respect to different defect types and image structures, by template feature clustering and pseudo color transferring. Our experiments show that the proposed approach indeed improves the readability of EL images and provides better visualization of solar cell defects.

We propose a new model to assess the effectiveness of camouflage in terms of perceived color difference and gradient magnitude. The “image color similarity index” (ICSI) and gradient magnitude similarity deviation (GMSD) were employed to analyze color and texture differences, respectively, between background and camouflage images. Information entropy theory was used to calculate weights for each metric, yielding an overall camouflage effectiveness metric. During the analysis process, both spatial and color perceptions of the human visual system (HVS) were considered, to mimic real-world observations. Subjective tests were used to compare our proposed method with previous methods, and our results confirmed the validity of assessing camouflage effectiveness based on perceived color difference and gradient magnitude.

Purpose This study aims to investigate the varying color-difference thresholds for textiles under various illuminants. Design/methodology/approach To determine color-difference thresholds, 108 fabric samples with widely ranging colors were spectrophotometrically measured and visually assessed under four illumination conditions (correlated color temperatures of 2,856 K and 6,504 K; illuminances of 100 lx and 2,000 lx). Findings The mean spectrophotometric color-difference threshold (solely based on the textile’s physical color attributes in machine vision) was 4.22 ΔE*ab; thus, a mechanically measured color difference between two textiles (regardless of illuminant) of less than 4.22 ΔE*ab indicated individuals’ inability to differentiate between two textiles. The calculated color-difference thresholds, which reflected the color differences perceived by people considering textiles’ physical color attributes and the environmental factors in which they were observed, were higher than the spectrophotometric thresholds. This meant that under various illuminants, people had greater difficulty perceiving color differences than mechanically measured spectrophotometric thresholds. Particularly, larger discrepancies between the two threshold types occurred under reddish illuminants of 2,856 K than under bluish-white illuminants of 6,504 K. Furthermore, the extent to which people perceived color differences as distinct from actual differences under illumination was larger for greenish-blue textiles than for textiles of other hues. Originality/value The determined spectrophotometric and calculative color-difference thresholds for textiles can help fashion companies predict consumers’ perceptions of the colors of their textile products under various illuminants and, accordingly, appropriately plan colors of products and create standards for color quality control.

Perceptual uniformity is a highly desirable property of colour spaces or colour difference measures where equal level in colour value difference should result in equal perceptual difference. Designing colour spaces or colour difference measures of perceptual uniformity is a long standing problem in colour science. This has become increasingly important with the growing popularity of high dynamic range (HDR) and wide colour gamut (WCG) cameras, content and displays. We design an efficient testing framework to evaluate perceptual uniformity by subjective just noticeable difference (JND) measurement at a wide range of luminance levels followed by coefficient of variation (CV) computation. We carry out subjective testing on RGB, $\mathrm{x}\mathrm{y}\mathrm{Y}, \mathrm{L}^{*}\mathrm{a}^{*}\mathrm{b}^{*}$, YCbCr, CIECAM02-UCS and ICtCp colour spaces and $\triangle$E2000 metric in ITU-R BT 2020 colour gamut across a wide range of luminance levels (from 0.01 to 500 nits) using a professional HDR/WCG display in a carefully controlled dark testing environment. Our results suggest that on average, the ICtCp space performs the best in the current test, but is still distant from achieving perceptual uniformity.

According to the color difference fabric dyeing measurement in production, partial color existed bad real-time problems.Color difference and color of RGB fabric images put forward by using the method of color distribution measurement of general, which needed rapid measurement of large area color.According to RGB distribution histogram of the image, the measured color with sample color similarity and distribution curve of characteristic value was calculated, obtained the RGB color model, and gives a method of a partial color judging.The experiment proves the feasibility of this measurement fabric color difference and the judgment method and effectiveness of partial color.Color cast detection method has overcome the limitations of the traditional methods of detecting image color cast.In order to partial color image detection is performed after correction, using a combination of gray world and a perfect reflection of the color correction method, to make up for the shortcomings of traditional method.The experimental results show that, the color deviation correction method of image analysis based on detection improved the reliability of color cast detection.The method uses characteristics of image analysis, it has universal applicability.

Detecting colour differences in warp‐knitting fabric is essential to ensure its quality. Machine vision algorithms are commonly used for automatic colour difference detection. However, the current algorithm directly extracts RGB (red, green, blue) colour stimulus values from the image without considering the image's colour attributes, leading to a significant error in colour difference detection. To solve this issue, a colour appearance model is introduced into the field of colour difference detection for warp‐knitted fabric. Initially, a colour appearance model based on CAM16 is established, and the critical parameters are calculated to obtain the colour appearance attribute of the fabric. The colour difference calculation formula is then constructed in the uniform colour space of CAM16‐UCS. A template selection algorithm based on principal component analysis was designed to select warp‐knitted cloth images with standard colours. Subsequently, a cloth colour difference detection algorithm was developed using the cloth colour profile model. The performance of the colour difference formula based on the colour profile model was evaluated using the PF/3 method. To compare the CIELab, CMC(2:1), and CIEDE2000 colour difference formulas, standardised residual sum of squares was used. The results indicated that the colour difference formula based on the colour‐appearance model is about 5.32% different from the visual colour difference perceived by the human eye. However, it can perform as well as the CMC(2:1) colour difference formula, which is widely used in the textile industry.

Similarity between percepts and concepts is used to accomplish many everyday tasks, e.g., object identification; so this similarity is widely used to construct geometrical spaces that represent stimulus qualities, but the intrinsic validity of the geometry, i.e., whether similarity operations support a particular geometry, is almost never tested critically. We introduce an experimental approach for equating relative similarities by setting perceived midpoints between pairs of stimuli. Midpoint settings are used with Varignon's Theorem to test the intrinsic geometry of a representation space, and its mapping to a physical space of stimuli. For perceptual color space, we demonstrate that geometrical structure depends on the mental representation used in judging similarity: An affine geometry was valid when observers used an opponent-color mental representation. Similarities based on a conceptual space of complementary colors thus power a geometric coordinate system. An affine geometry implies that similarity can be judged within straight lines and across parallel lines, and its neural coding could involve ratios of responses. We show that this perceptual space is invariant to changes in illumination color, providing a formal justification to generalize color constancy results measured for color categories, to all of color space. The midpoint measurements deviate significantly from midpoints in the extensively used “uniform” color spaces CIELAB and CIELUV, showing that these spaces do not provide adequate metric representation of perceived colors. Our paradigm can thus test for intrinsic geometrical assumptions underlying the representation space for many perceptual modalities, and for the extrinsic perceptual geometry of the space of physical stimuli.