• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• ETRI Journal
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• v.38 no.4
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• pp.776-786
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• 2016

The fairly recent standard of equipping mobile devices with advanced imaging sensors has opened the possibility of conveniently diagnosing skin conditions, anywhere, anytime. For this application, we attempted to estimate skin conditions from a skin image taken by a mobile handheld camera. To estimate the skin conditions, we specifically identified three skin features (pigmentation, pores, and roughness) that can be measured quantitatively from a skin image. The experimental data indicate that the existing thresholding methods are inappropriate for extracting the pigmentation and pore skin features. Thus, we propose a new line-fitting based thresholding method for skin feature detection. We thoroughly evaluated our proposed skin condition estimation method using our skin image database. The experimental results show that our proposed thresholding method can better determine the threshold leading to the most visually plausible detection, when compared to existing methods. We also confirmed that skin conditions can be feasibly estimated using a common mobile handheld camera (for example, a smartphone).

• Korean Institute of Interior Design Journal
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• v.17 no.2
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• pp.30-38
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• 2008

It has been stated that the outer skin of an architecture should be related to and express the interior programs. It was rather moral issue than practicality. In contemporary urban cities, this nicely-linked relationship between exterior and interior has become much more complex and, in many cases, is no more valid. It tends that contemporary architectural skin is somehow separately developed and has its own logic to be independent from what is inside. This research focuses on these sort of logical design process to make unique image of skin in which conceptual thinking, spatialization and materialization are mixed together. More specifically this study articulates' expandable patternization process' based on the notion that it has a crucial role to systematically construct an image of skin. Expandable patternization has a couple of stages to complete an architectural skin. The first element is a single unit and the second is organization or arrangement of units based on a logical process. Lastly, the third is spatialization after relating the skin to the interior programs as well as environmental surroundings. It is found that, although, in most related projects, the architect or designer has followed his or her own preference or design tendencies, many skin projects has based the given unique characteristics from the beginning. This study concludes that skin design is not just an image making, but has an important role to amalgamate various aspects of an architectural projects: programs, concept of architect, environment, structure as well as image.

• Journal of Biomedical Engineering Research
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• v.28 no.2
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• pp.195-198
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• 2007

In this paper, we introduce a digital cross-polarization and fluorescent color imaging system for quantitative evaluation of skin lesions. We describe the characterization of the imaging systems and the quantitative image analysis methods to show the feasibility for quantitative evaluation of skin lesions. The polarization color image was used to compute erythema and melanin index image which are useful for quantitative evaluation of pigmentation and vascular skin lesions, respectively. The fluorescent color image was used to quantitatively evaluate "sebum" and "vitiligo". In quantitative evaluation of various skin lesions, we confirmed the clinical efficacy of the imaging systems for dermatological applications. Finally, we sure that the imaging systems can be utilized as important assistant tools for the evaluation of skin lesions by providing reproducible quantitative result for widely distributed skin lesions.

• 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.

• Journal of Advanced Information Technology and Convergence
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• v.8 no.2
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• pp.47-58
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• 2018

In this paper, we propose a method for skin condition analysis using a camera module embedded in a smartphone without a separate skin diagnosis device. The type of skin disease detected in facial image taken by smartphone is acne, pigmentation, blemish and flush. Face features and regions were detected using Haar features, and skin regions were detected using YCbCr and HSV color models. Acne and flush were extracted by setting the range of a component image hue, and pigmentation was calculated by calculating the factor between the minimum and maximum value of the corresponding skin pixel in the component image R. Blemish was detected on the basis of adaptive thresholds in gray scale level images. As a result of the experiment, the proposed skin condition analysis showed that skin diseases of acne, pigmentation, blemish and flush were effectively detected.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.432-435
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• 2004

The skin color of a human being is the important memory color influencing image quality for color display. Therefore, in this paper, the preferred skin color axis is defined on HSV color space by analyzing some previous research, and the preferred skin color reproduction algorithm is performed by rotating the center axis of skin distribution of an input image to the preferred skin color axis.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.16 no.1
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• pp.47-63
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• 1990

Image analyser was used to understand the condition of skin surface and to evaluate the efficacy of cosmetic treatment. It was unsatisfactory to analyse skin surface structure although several methods using image analyser had been presented. We developed the new system composed of image analyser and Tandem Scanning Reflected Light Microscope (TSRLM) having the remarkable optical sectioning property as image input device. By using this new system, we quantitatively measured the change of skin surface, the depth and width of furrow in micron unit, resulted by cosmetic treatments. And also three dimensional image of skin was reconstructed with serial sectioned images, which were captured through TSRLM, for better understanding of the effect of cosmetic treatment. It was found that skin relief was more easily understood and the change of skin surface caused by cosmetic treatment was more accurately measured by using this system. In addition, we was also aware of the possibility of in vivo direct measurement of skin furrow without replica. It was conceivable that our system could be applicable for study of cosmetic effects further.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.141-156
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• 2013

In this paper we propose a method to detect human faces in color images. Many existing systems use a window-based classifier that scans the entire image for the presence of the human face and such systems suffers from scale variation, pose variation, illumination changes, etc. Here, we propose a lighting insensitive face detection method based upon the edge and skin tone information of the input color image. First, image enhancement is performed, especially if the image is acquired from an unconstrained illumination condition. Next, skin segmentation in YCbCr and RGB space is conducted. The result of skin segmentation is refined using the skin tone percentage index method. The edges of the input image are combined with the skin tone image to separate all non-face regions from candidate faces. Candidate verification using primitive shape features of the face is applied to decide which of the candidate regions corresponds to a face. The advantage of the proposed method is that it can detect faces that are of different sizes, in different poses, and that are making different expressions under unconstrained illumination conditions.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.193-196
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• 2009

Most of studies adopt a fixed skin color model to segment skin color region in a single image. The methods, however, result in low detection rates or high false positive error rates since the distribution of skin color is varies depending on the characteristics of input image. For the effective skin color segmentation, therefore, we need a adaptive skin color model which changes the model depending on the color distribution of input image. In this paper, we propose a novel adaptive skin color segmentation algorithm consisting of 2 stages which results in both high detection rate and low false positive error rate.