• Journal of the Korea Fashion and Costume Design Association
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• v.9 no.2
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• pp.179-192
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• 2007

This study is to search for a new area and a new kind of occupation for fashion in the field of multi-media such as a movie, drama, mass-culture and advertisement, as a basic investigation to improve a potential of a development of fashion in the future, to keep in step with the trend of the changes under the environment of cultural renovation. In this reports, the field and vision of new contents in fashion will be proposed. The definition and environment of multi-media were examined, and various kinds and work areas of new fashion specialists were defined. 12 professionals in each fields relative to multi-media were selected and the status of affairs, problems and requirements of fashion specialists were investigated through the in-depth interviews with them. Finally new fields and visions were suggested on it's future course. The kind of fashion-specialist on the field related to multi-media were like this: 1. Fashion-stylist, Art-director and Image-maker for star on the field of Video industry. 2. Fashion-illustrator for making animation-game character, Avatar fashion product designer and Internet shopping buyer for Online-business industry. 3. Fashion PR director, Fashion-photo stylist for Advertisement industry. 1 classified new field on the field related to multi-media as the above, and I researched the role of specialist in each field and the status of affairs and vision.

• Smart Media Journal
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• v.12 no.11
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• pp.125-133
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• 2023

Traditional attire has assumed a pivotal role within the contemporary fashion industry. The objective of this study is to construct a computer vision model tailored to the recognition of traditional costumes originating from five distinct countries, namely India, Korea, Japan, Tanzania, and Vietnam. Leveraging a dataset comprising 1,608 images, we proceeded to train the cutting-edge computer vision model YOLOv8. The model yielded an impressive overall mean average precision (MAP) of 96%. Notably, the Indian sari exhibited a remarkable MAP of 99%, the Tanzanian kitenge 98%, the Japanese kimono 92%, the Korean hanbok 89%, and the Vietnamese ao dai 83%. Furthermore, the model demonstrated a commendable overall box precision score of 94.7% and a recall rate of 84.3%. Within the realm of the fashion industry, this model possesses considerable utility for trend projection and the facilitation of personalized recommendation systems.

• Communications of the Korean Institute of Information Scientists and Engineers
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• v.28 no.7
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• pp.70-78
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• 2010

• International Journal of Costume and Fashion
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• v.4 no.2
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• pp.1-9
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• 2004

This study examined the role of Korea, Japan and China (including brief information about Hong Kong) in the context of their changing fashion industries and respective, regional and global identities.

• International Journal of Costume and Fashion
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• v.3
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• pp.25-36
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• 2003

This paper discusses developments in fashion practice, and fashion education, reviewing developments throughout Asia. Reference is made to the historic origins of the clothing trade and efforts made on behalf of industry to educate its personnel. Current challenges are addressed, and a case study based on experience in Asia is provided.

• Proceedings of the Korea Fashion and Costume Design Association Conference
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• autumn
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• pp.28-30
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• 2004

• Proceedings of the Costume Culture Conference
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• 2004.10a
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• pp.19-20
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• 2004

• Journal of Fashion Business
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• v.7 no.5
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• pp.17-31
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• 2003

$Premi\grave{e}re$ Vision is a leading textile collection held since 1973. In this study, textiles for 2004/05 F/W season in $Premi\grave{e}re$ Vision were analyzed at the various point of view, such as general trend, sub-theme, fiber content, color trend, structure, yarn trend, pattern, texture, decoration, finishing and other technical treatment, and functionality. There were three general trends; natural/ecology, geometry, and combination. Natural color and texture were widely used over the fabric exhibited, and irregularities expressed the natural trend of textiles. Geometric patterns were used for knit as well as for woven. Geometry expressed by various methods such as weaving, knitting, printing, shearing, embossing, and etc. However, geometry shown in this season was not a clear form expressed by weaving, but a blurry, irregular form expressed by various other methods such as knitting. The combination among heterogeneous fibers, yarns, colors, images, and textures was usual, however, the harmony among them was accomplished.

• Journal of Fashion Business
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• v.13 no.6
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• pp.20-30
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• 2009

The purpose of this study is to develop the effective customized elderly fashion marketing process based on the web site, where older customer will be able to choose various fabrics and to try them out. This aims to establish new prototype of internet shopping mall for customized elderly fashion clothing. In this study, new method of product presentation on the online shopping mall is proposed to offer product information through 3D virtual reality. With the online shopping mall(SATC Mall) as a showcase, we presented virtual mapping system so that it enable the customers to select the fabrics and to see exactly how chosen fabric will look when applied to image of clothing. As an initial test of the application of simulation to measure 3D visualization of product, mapping software Vision Easy Map Pro Version 6.0(NedGraphics) Vision Easy Map Viewer Version 5.0(NedGraphics) were chosen and applied. By using this mapping system, the fabric change of the apparel product could be made on the internet shopping web site. However, this approach has been successful applied for presenting and customizing garment products. Future research will focus on the integration of mapping coordination into SATC Mall.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.7-13
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• 2021

Machine vision is a technology that helps the computer as if a person recognizes and determines things. In recent years, as advanced technologies such as optical systems, artificial intelligence and big data advanced in conventional machine vision system became more accurate quality inspection and it increases the manufacturing efficiency. In machine vision systems using deep learning, the image quality of the input image is very important. However, most images obtained in the industrial field for quality inspection typically contain noise. This noise is a major factor in the performance of the machine vision system. Therefore, in order to improve the performance of the machine vision system, it is necessary to eliminate the noise of the image. There are lots of research being done to remove noise from the image. In this paper, we propose an autoencoder based machine vision system to eliminate noise in the image. Through experiment proposed model showed better performance compared to the basic autoencoder model in denoising and image reconstruction capability for MNIST and fashion MNIST data sets.