• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.267-275
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• 2021

Skin condition is an important concern in beauty aspect. This study considered a rough skin condition analysis method that beauty industry workers can do through visual observation or skin condition images taken with smartphones. First, studies that combine subjective and objective evaluations were selected among degree papers and academic papers searched by keywords related to 'skin condition' in the Research Information Sharing Service(RISS). Among them, papers that derive correlations with visually verifiable factors were selected. Next, the relationship between factors that match subjective skin condition and objective skin measurement results and factors that can be visually identified on the skin surface was investigated. According to the study, the most suitable factor for matching subjective and objective evaluations was 'oil volume', which is significantly related to 'pore', 'skin texture' and 'erythema', which can be visually checked on the skin surface. It is believed that a rough skin condition analysis will be possible using this.

• Journal of The Geomorphological Association of Korea
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• v.27 no.3
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• pp.87-103
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• 2020

The purpose of this study is to evaluate the risk of cropland and man-made infrastructures in a landslide-prone area using a GIS-based method. To achieve this goal, a landslide inventory map was prepared based on aerial photograph analysis as well as field observations. A total of 550 landslides have been counted in the entire study area. For model analysis and validation, extracted landslides were randomly selected and divided into two groups. The landslide causative factors such as slope, aspect, curvature, topographic wetness index, elevation, forest type, forest crown density, geology, land-use, soil drainage, and soil texture were used in the analysis. Moreover, to identify the correlation between landslides and causative factors, pixels were divided into several classes and frequency ratio was also extracted. A landslide susceptibility map was constructed using a bayesian predictive model (BPM) based on the entire events. In the cross validation process, the landslide susceptibility map as well as observation data were plotted with a receiver operating characteristic (ROC) curve then the area under the curve (AUC) was calculated and tried to extract a success rate curve. The results showed that, the BPM produced 85.8% accuracy. We believed that the model was acceptable for the landslide susceptibility analysis of the study area. In addition, for risk assessment, monetary value (local) and vulnerability scale were added for each social thematic data layers, which were then converted into US dollar considering landslide occurrence time. Moreover, the total number of the study area pixels and predictive landslide affected pixels were considered for making a probability table. Matching with the affected number, 5,000 landslide pixels were assumed to run for final calculation. Based on the result, cropland showed the estimated total risk as US $ 35.4 million and man-made infrastructure risk amounted to US $ 39.3 million.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.275-284
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• 2008

With increases in recent security requirements, biometric technology such as fingerprints, faces and iris recognitions have been widely used in many applications including door access control, personal authentication for computers, internet banking, automatic teller machines and border-crossing controls. Finger vein recognition uses the unique patterns of finger veins in order to identify individuals at a high level of accuracy. This paper proposes new device and methods for touchless finger vein recognition. This research presents the following five advantages compared to previous works. First, by using a minimal guiding structure for the finger tip, side and the back of finger, we were able to obtain touchless finger vein images without causing much inconvenience to user. Second, by using a hot mirror, which was slanted at the angle of 45 degrees in front of the camera, we were able to reduce the depth of the capturing device. Consequently, it would be possible to use the device in many applications having size limitations such as mobile phones. Third, we used the holistic texture information of the finger veins based on a LBP (Local Binary Pattern) without needing to extract accurate finger vein regions. By using this method, we were able to reduce the effect of non-uniform illumination including shaded and highly saturated areas. Fourth, we enhanced recognition performance by excluding non-finger vein regions. Fifth, when matching the extracted finger vein code with the enrolled one, by using the bit-shift in both the horizontal and vertical directions, we could reduce the authentic variations caused by the translation and rotation of finger. Experimental results showed that the EER (Equal Error Rate) was 0.07423% and the total processing time was 91.4ms.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.396-404
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• 2014

The field of searching clothing, which is very difficult due to the nature of the informal sector, has been in an effort to reduce the recognition error and computational complexity. However, there is no concrete examples of the whole progress of learning and recognizing for cloth, and the related technologies are still showing many limitations. In this paper, the whole process including identifying both the person and cloth in an image and analyzing both its color and texture pattern is specifically shown for classification. Especially, deformable search descriptor, LBPROT_35 is proposed for identifying the pattern of clothing. The proposed method is scale and rotation invariant, so we can obtain even higher detection rate even though the scale and angle of the image changes. In addition, the color classifier with the color space quantization is proposed not to loose color similarity. In simulation, we build database by training a total of 810 images from the clothing images on the internet, and test some of them. As a result, the proposed method shows a good performance as it has 94.4% matching rate while the former Dense-SIFT method has 63.9%.

• Conservation Science in Museum
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• v.31
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• pp.71-103
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• 2024

The National Museum of Korea Conservation science division conducted a precise diagnosis and a non-destructive investigation to comprehensively assess the overall damage of the Twin-lion stone lantern from the Godalsa Temple site, Yeoju to be placed on display in the museum's outdoor stone garden, then reviewed the relevant conservation and management plan and applied conservation treatment to the artifact. The museum carried out the treatment in the following order: precise diagnosis; dismantling of the previously-restored part of the roof stone; reinforcement and restoration of the roof structure with new stone; restoration of the previously-restored part of the lantern's support stone (jungseok); surface texture treatment to the restored area; cleaning (basic, laser); and color matching. The previously-restored part of the roof stone was removed and restored with new stone material, based on the results of a safety diagnosis regarding the separation at the said part. Granite from the Sangju area was selected as the material for the restoration in consideration of the results of mineral analysis as well as the surface color and particle size. The new stone was divided into three pieces based on the descending edges of the octagonal roof structure and joined together using epoxy resin. The structure was further strengthened by inserting titanium rods. It is expected that the status diagnosis and conservation treatment of the twin-lion stone lantern from the Godalsa Temple site in Yeoju will be used as a reference for the future conservation and management of outdoor displays of stone cultural heritage.