• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.37-44
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• 2011

This paper proposes a confusion-line separating algorithm in a CIE Lab color space using color segmentation for protanopia and deuteranopia. Images are segmented into regions by grouping adjacent pixels with similar color information using the hue components of the images. To this end, the region growing method and the seed points used in this method are the pixels that correspond to peak points in hue histograms that went through a low pass filter. In order to establish a color vision deficiency (CVD) confusion line map, we established 512 virtual boxes in an RGB 3-D space so that boxes existing on the same confusion line can be easily identified. After that, we checked if segmented regions existed on the same confusion line and then performed color adjustment in an CIE Lab color space so that all adjacent regions exist on different confusion lines in order to provide the best color identification effect to people with CVDs.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2020.11a
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• pp.64-65
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• 2020

With the modernization fund of the government, construction of new passenger ships make the level of interior design is improved, but the space where the crew is working is still inadequate. This study is to investigate the color environment of the Bridge Deck and Engine Room among working spaces of passenger ships. It aims to improve the mental health of crews and set up a safe working environment by presenting color design, and suggest the specificity of ship's working space and color value with matching the color environment.

• Journal of KIISE
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• v.42 no.10
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• pp.1294-1302
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• 2015

A lot of image quality assessment metrics that can precisely reflect the human visual system (HVS) have previously been researched. The Structural SIMilarity (SSIM) index is a remarkable HVS-aware metric that utilizes structural information, since the HVS is sensitive to the overall structure of an image. However, SSIM fails to deal with color difference in terms of the HVS. In order to solve this problem, the Structural and Hue SIMilarity (SHSIM) index has been selected with the Hue, Saturation, Intensity (HSI) model as a color space, but it cannot reflect the HVS-aware color difference between two color images. In this paper, we propose a new image quality assessment method for a color image by using a CIE Lab color space. In addition, by using a support vector machine (SVM) classifier, we also propose an optimization system for applying optimal metric according to the types of distortion. To evaluate the proposed index, a LIVE database, which is the most well-known in the area of image quality assessment, is employed and four criteria are used. Experimental results show that the proposed index is more consistent with the other methods.

• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.473-481
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• 2005

In this paper, we propose an image segmentation method preserving object's boundaries by using the number of quantized colors and merging regions using adaptive threshold values. First of all, the proposed method quantizes an original image by a vector quantization and the number of quantized colors is determined differently using PSNR each image. We obtain initial regions from the quantized image, merge initial regions in CIE Lab color space and RGB color space step by step and segment the image into semantic regions. In each merging step, we use color distance between adjacent regions as similarity-measure. Threshold values for region-merging are determined adaptively according to the global mean of the color difference between the original image and its split-regions and the mean of those variations. Also, if the segmented image of RGB color space doesn't split into semantic objects, we merge the image again in the CIE Lab color space as post-processing. Whether the post-processing is done is determined by using the color distance between initial regions of the image and the segmented image of RGB color space. Experiment results show that the proposed method splits an original image into main objects and boundaries of the segmented image are preserved. Also, the proposed method provides better results for objective measure than the conventional method.

• 대한치과보철학회:학술대회논문집
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• 1996.11a
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• pp.61-62
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• 1996

• Journal of Information Display
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• v.12 no.4
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• pp.179-190
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• 2011

A complete analysis of the color-viewing-angle properties of different displays is presented herein using color-viewing-angle measurements made with a Fourier-optics system. The color gamut in the CIE u'v' chromatic plane was computed for all the viewing angles. The introduction of the lightness using the $L^*a^*b^*$ color space allowed a more precise analysis of the emissive properties of each display. The displays can be directly compared using a common reference. The viewing-angle dependence can be analyzed in full detail using the on-axis values as reference. The gravity center behavior and area of the color hull were computed for a more precise evaluation and comparison.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.7
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• pp.340-353
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• 2008

A computational color palette extraction model is introduced to describe paint brush objectively and efficiently. In this model, a color palette is defined as a minimum set of colors in which a painting can be displayed within error allowance and extracted by the two step processing of color grouping and major color extraction. The color grouping controls the resolution of colors adaptively and produces a basic color set of given painting images. The final palette is obtained from the basic color set by applying weighted k-means clustering algorithm. The extracted palettes from several famous painters are displayed in a 3-D color space to show the distinctive palette styles using RGB and CIE LAB color models individually. And the two experiments of painter classification and color transform of photographic image has been done to check the performance of the proposed method. The results shows the possibility that the proposed palette model can be a computational color analysis metric to describe the paint brush, and can be a color transform tool for computer graphics.

• Journal of the Korean Graphic Arts Communication Society
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• v.25 no.1
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• pp.121-133
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• 2007

The CMS(color management system) software was to enable consistent color reproduction from original to reproduction. The CMS was to create RGB monitor and printer characterization profiles and then use the profiles for device independent color transformation. The implemented CMM(color management module) used the CIELAB color space for the profile connection. Various monitor characterization model was evaluated for proper color transformation. To construct output device profile, SLI(sequential linear interpolation) method was used for the color conversion from CMYK device color to device independent CIELAB color space and tetrahedral interpolation method was used for backward transformation. UCR(under color removal) based black generation algorithm was used to construct CIELAB to CMYK LUT(lookup table). When transforming the CIE Lab colour space to CMYK, it was possible to involve the gray revision method regularized in the brightness into colour transformation process and optimize the colour transformation by black generation method based on UCR technique. For soft copy colour proofing, evaluating several monitor specialism methods showed that LUT algorithm was useful. And it was possible to simplify colour gamut mapping by constructing both the look-up table and the colour gamut mapping algorithm to a reference table.

• Restorative Dentistry and Endodontics
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• v.32 no.1
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• pp.19-27
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• 2007

The purpose of this study is to develope new dental color-space system. Twelve kinds of dental composites and one kind of dental porcelain were used in this study. Disk samples (15 mm in diameter, 4 mm in thickness) of used materials were made and sample's CIE $L^*a^*b^*$ value was measured by Spectrocolorimeter (MiniScan XE plus, Model 4000S, diffuse/$8^{\circ}$ viewing mode, 14.3 mm Port diameters, Hunter Lab USA) The range of measured color distribution was analyzed. All the data were applied in the form of T### which is expression unit in CNU Cons Dental Color Chart. The value of $L^*$ lies between 80.40 and 52.70. The value of $a^*$ are between 10.60 and 3.60 and $b^*$ are between 28.40 and 2.21. The average value of $L^*$ is 67.40, and median value is 67.30. The value of $a^*$ are 2.89 and 2.91 respectively. And for the $b^*$, 14.30 and 13.90 were obtained. The data were converted to T### that is the unit count system in CNU-Cons Dental Color Chart. The value of $L^*$ is converted in the first digit of the numbering system. Each unit is 2.0 measured values. The second digit is the value of $a^*$ and is converted new number by 1.0 measured value. For the third digit $b^*$ is replaced and it is 2.0 measured unit apart. T555 was set to the value of $L^*$ ranging from 66.0 to 68.0, value of $a^*$ ranging from 3 to 4 and $b^*$ value ranging from 14 to 16.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.132-140
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• 2016

In this paper, we propose boundary-preserving stereo matching method based on adaptive disparity adjustment using confidence region detection. To find the initial disparity map, we compute data cost using the color space (CIE Lab) combined with the gradient space and apply double cost aggregation. We perform left/right consistency checking to sort out the mismatched region. This consistency check typically fails for occluded and mismatched pixels. We mark a pixel in the left disparity map as "inconsistent", if the disparity value of its counterpart pixel differs by a value larger than one pixel. In order to distinguish errors caused by the disparity discontinuity, we first detect the confidence map using the Mean-shift segmentation in the initial disparity map. Using this confidence map, we then adjust the disparity map to reduce the errors in initial disparity map. Experimental results demonstrate that the proposed method produces higher quality disparity maps by successfully preserving disparity discontinuities compared to existing methods.