• Journal of the Korean Home Economics Association
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• v.38 no.11
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• pp.127-135
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• 2000

The natural dyestuff, grape skin dye was manufactured from grape skin by boiling in 0.1% HCI solution, eliminating the sugar and powdering in freeze dryer. Cotton, wool, silk and nylon fabrics were dyed under several conditions using the manufactured grape skin dye to investigate the dyeability and color fastness. Dyeing was operated in acidic dyebath of pH 4 because the grape skin is anionic. The color of dyed fabrics were affected on temperature of dyeing solution. Under 80$\^{C}$, the color of dyed fabrics were red or violet, but changed to brown in laundering. Above 100$\^{C}$, the color were brown and safe in laundering. Dyeabilities on wool, silk, and nylon fabrics were good, especially silk fabrics were dyed deeper than others. Dyeability was developed with concenturation of dyeing solution. All the dyed fabrics were excellent in color fastness to crocking and laundering. Light fastness was low to moderate. The light fastness of dyed nylon fabrics were as poor as grade 1, but they could be improved to grade 3∼4 by aftertreatment with gallic acid.

• Archives of Plastic Surgery
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• v.38 no.6
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• pp.725-732
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• 2011

Purpose: Skin grafting is one of the most commonly used methods in reconstructive plastic surgery field, but complications such as color change, contracture or hypertrophy are common problems. However, pathophysiology of the color change after skin graft is not yet determined and no animal model is established. Methods: Full thickness skin grafts were performed on the dorsum of C57BL/6 mice. Serial chronological gross inspection for color change and pigmentation were examined. Melanin pigments were traced by Fontana-Masson staining and semi-quantitative analysis was performed. In addition, immunohistochemical staining of S-100, Micropthalmia related Transcription Factor (MITF) and Melan-A antibodies were also performed to observe melanocytes and their changes. Results: After skin graft, color change and pigment spots were observed in the graft. Fontana-Masson staining showed melanin pigments in the epidermal and dermal layers in all mice. Immunohistochemistry staining to S-100, MITF, Melan-A antibodies showed melanocytes at the basal layer of epidermis and dermis. Conclusion: In conclusion, we have established an animal model for skin pigmentation after skin graft. We believe this study may be useful in understanding of the behavior of melanocytes after skin graft.

• The Research Journal of the Costume Culture
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• v.9 no.2
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• pp.308-318
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• 2001

The purpose of this study is to investigate the clothing interest, the preference for clothing color, in men of their 20s and 30's. The data were obtained from questionnaires completed by 546 men in their 20's and 30s. The SPSS package was used for data analysis which includs factor analysis, t-test, x²-test, Scheffe test, and Cronbach's α to measure the reliability. This study shows the following results. 1. The interest for clothing color was related to individual factors. (age, marriage status, personality). 2. men in their 20's and 30's preferred neutral color, blue, yellow. 3. Men in their 20s and 30s liked monochromatic match in clothing color. 4. The preference for clothing color were appeared to be influenced by the age, personality, skin color.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.29-36
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• 2012

In this paper, we suggest a new method of detecting human skin-color regions from three-dimensional static or dynamic stereoscopic images by effectively integrating depth and color features. The suggested method first extracts depth information that represents the distance between a camera and an object from input left and right stereoscopic images through a stereo matching technique. It then performs labeling for pixels with similar depth features and determines the labeled regions having human skin color as actual skin color regions. Our experimental results show that the suggested skin region extraction method outperforms existing skin detection methods in terms of skin-color region extraction accuracy.

• Journal of the Korea Society of Computer and Information
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• v.14 no.4
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• pp.19-30
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• 2009

In this paper, We propose a method with which we can detect facial components and face in input image. We use eye map and mouth map to detect facial components using eyes and mouth. First, We find out eye zone, and second, We find out color value distribution of skin region using the color around the eye zone. Skin region have characteristic distribution in YCbCr color space. By using it, we separate the skin region and background area. We find out the color value distribution of the extracted skin region and extract around the region. Then, detect mouth using mouthmap from extracted skin region. Proposed method is better than traditional method the reason for it comes good result with accurate mouth region.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.520-523
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• 2009

There are many ways to judge whether the input image is adult-image or not. Until now, adult image detection has been examined by the ratio of skin area in full image. In this paper, we propose a method to extract skin region in YCbCr. Skin region shows unique distribution in YCbCr, and we will separate the skin region from background using the distribution. First, we are going to find Eye zone using Eye-Map. Then we will find out the color value for the distribution of skin region using the color of Eye zone. Next, we will find the distribution of the area through the skin region in full-image.

• The Journal of Korean Medicine
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• v.37 no.3
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• pp.27-38
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• 2016

Objectives: According to Korean Medicine theory, the skin color of LU10 serves as a diagnostic clue to dyspeptic symptoms. The aims of this study were (1) to find the difference of skin color in LU10 region between functional dyspepsia (FD) and healthy control (HC) and (2) to examine the relationship between LU10 skin color parameters and dyspeptic symptoms. Methods: 39 participants (29 FD and 10 HC) have participated in this study. They were asked to complete gastrointestinal scale (GIS), gastrointestinal symptom rating scale (GSRS), Nepean dyspepsia index (NDI), functional dyspepsia-related quality of life (FD-QoL), visual analogue scale (VAS) for dyspeptic symptoms, food retention questionnaire (FRQ) and cold heat questionnaire (CHQ). $L^*$ (luminance), $a^*$ (red-green balance) and $b^*$ (yellow-blue balance) values of LU10 region were calculated through digital images of the participant's hand. Then we evaluated test-retest reliability of $L^*$, $a^*$ and $b^*$ values of LU10 region. Additionally, we compared $L^*$, $a^*$ and $b^*$ values of LU10 between FD and HC, and examined the relationship between LU10 color parameters and seven questionnaires scores. Results: Only $L^*$ values in LU10 region were significantly higher in FD compared with HC. GIS scores and the subset scores of NDI had a positive correlation with $L^*$ values significantly. Correlation coefficients of test-retest reliability of skin color measurement of LU10 ranged from 0.871 to 0.936 representing very strongly statistically significant (P<0.001). Conclusions: We confirmed the difference of skin color in LU10 region between FD and HC, and relationship between LU10 skin color parameters and dyspeptic symptoms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9C
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• pp.1258-1268
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• 2004

We propose a new fast, algorithm which is used for detecting frontal face in the frequency domain based on human skin-color using OCT coefficient of dynamic image compression and skin color information. The region where each pixel has a value of skin-color were extracted from U and V value based on DCT coefficient obtained in the process of Image compression using skin-color map in the Y, U, V color space A morphological filter and labeling method are used to eliminate noise in the resulting image We propose the algorithm to detect fastly human face that estimate the directional feature and variance of luminance block of human skin-color Then Extraction of face was completed adaptively on both background have the object analogous to skin-color and background is simple in the proposed algorithm The performance of face detection algorithm is illustrated by some simulation results earned out on various races We confined that a success rate of 94 % was achieved from the experimental results.

• Journal of Digital Contents Society
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• v.19 no.7
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• pp.1397-1403
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• 2018

Today, modern people perceive skin care as part of their physical health care, and acne is a common skin disease problem that is found on the face. In this paper, an effective acne detection algorithm using CIE $L^*a^*b^*$ color space has been proposed. It is red when the pixel value of the component image $a^*$ is a positive number, so it is suitable for detecting acne in skin image. First, the skin image based on the RGB color space is subjected to light compensation through color balancing, and converted into a CIE $L^*a^*b^*$ color space. The extracted component image $a^*$ was normalized, and then the skin and acne area were estimated with the threshold values. Experimental results show that the proposed method detects acne more effectively than the conventional method based on brightness information, and the proposed method is robust against the reflected light source.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.107-115
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• 2009

Skin color detection is used to classify input pixels into skin and non skin area, and it requires the classifier to have a high classification rate. In previous work, most classifiers used single color model for skin color detection. However the classification rate can be increased by using more than one color model due to the various characteristics of skin color distribution in different color models, and the MLP is also invested as a more efficient classifier with less parameters than other classifiers. But the input dimension and required parameters of MLP will be increased when using two color models in skin color detection, as a result, the increased parameters will cause the huge teaming time in MLP. In this paper, we propose a MLP based classifier with less parameters in two color models. The proposed partially connected MLP based on two color models can reduce the number of weights and improve the classification rate. Because the characteristic of different color model can be learned in different partial networks. As the experimental results, we obtained 91.8% classification rate when testing various images in RGB and CbCr models.