• The Research Journal of the Costume Culture
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• v.9 no.5
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• pp.734-746
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• 2001

The purpose of this study is to investigate the influence of women's self-image on image evaluation of clothing self-image, and on their selection of clothing styles by situations. The subject of investigation was 500 women above 20 living in Daegu. 6 types of clothing styles including classic, casual, elegant, dramatic, romantic, and mannish and 7 social situations including shopping near house, shopping in a busy street, cultural center, wedding ceremony, dinning out, alumni meeting or fraternity meeting, and couples meeting were used for this study. Data analysis was performed using SPSS package, which included factor analysis, reliability test, cluster analysis, ANOVA, and X²-test. The results are summarized as follows. 1. Adult women could be classified into 4 groups such as the passive mannish, the passive feminine, the active mannish, and the active feminine by their self-images. 2. There were different opinions on each clothing style by self-image. In the image of each clothing style by self-image groups, the passive feminine group considered classic style having effect to make people look tall, mature and elegant style to make people look active and charming. Also, they rated the boldness of dramatic style and the activeness of mannish style high. The active feminine group estimated the boldness of mannish style high. 3. Selection of clothing style differed according to various situations. More formal the situation was, more classic style tended to be selected and for less formal situation, mannish style was selected.

• Journal of the Korean Institute of Rural Architecture
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• v.25 no.2
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• pp.1-8
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• 2023

This study reveals the absence of standard design guidelines by region through investigation and analysis centered on public design cases by region in Jeollabuk-do and design elements that can reflect the integration, identity, and diversity of public design in each region by five regions. Through this, the following conclusions could be obtained. First, to improve the quality of street facilities in Jeollabuk-do, the design elements (design motif, color, pattern) applicable to the standard design were analyzed by dividing them into five regions. As a design motif, it was possible to extract patterns containing straight lines, sophistication, dignity, and smartness. In the Northeast region, it is comfortable with the motif of the mountain ridge reflecting geographical characteristics, and it can be extracted elements that contain warm and natural colors. In the southeastern region, patterns that reflect design elements were extracted by applying safe, lively, and peaceful colors with the design motif of curves that blend nature and agriculture. In the southwestern region, design pattern elements that highlight nature, history, and culture were extracted with various cultural assets and natural greenery as motifs. Lastly, in the Saemangeum region, the ocean flow and greenery could be used as a design motif to reflect a positive, clear, future-oriented image in the design spot zones by region. Second, based on the standard design elements (design motive, color, pattern) by region extracted for the standard design development of street facilities in each region in Jeollabuk-do, an integrated zone(Form, structure, material, color, functional element) to which regional design guidelines can be applied. Third, an integrated zone (form, structure, material, color, functional elements) was composed. In addition, design spot zones (patterns and colors in city and county units) that can contain the diversity and identity of each region were designated. By designating design spot zones (patterns and colors in city and county units) that can contain the diversity and identity of each region, standard design development plans (integrated pillars, jaywalking prevention fences, roundabouts (urban type, rural type), street trees) Eight standard designs, including protective covers, street planters, flat benches, light benches, visual media for user guidance, and parking zones for personal mobile devices) were presented.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1383-1386
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• 1987

This paper concerns, the method for velocity of dynamic Image on two dimensional sequence Image which can be obtained from two sample lines on the street. The velocity of a single moving object Is measured by the number of total frame which Is required when an automobile passes over the second sample line through the first sample line. The measured results show that the velocity error Is less than 5% comparing with the value measured by X-band speed gun.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.147-150
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• 2001

This paver has presented the architecture and function of the traffic signal enforcement system to detect and capture a image of the violating car in the street intersection. Also in the paper, the algorithm and method of detecting the violation car have been presented and the microwave detection method has been explained. And then this paper has showed the operation software interface for system and presented the experiment data carried out in the field.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.2
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• pp.227-238
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• 2002

The purpose of this study is to investigate the influence of women's life-style on image evaluation of clothing styles, and on their selection of clothing styles by situations. The subject of investigation was 441 women above 20 living in Daegu. 6 types of clothing styles including classic, casual, elegant, dramatic, romantic, and mannish and 7 social situations including shopping near house, shopping in a busy street, cultural center, wedding ceremony, eating out, alumni meeting or fraternity meeting, and couples meeting were used for this study. Data analysis was performed using SPSS package, which included factor analysis, reliability test, cluster analysis, frequency, percentage, ANOVA, and $\chi$$^2$-test. The results are summarized as follows; 1. Adult women could be classified into 5 groups including activist, in-activist, the leisured well-off, the wholesomely economical, and the appearance showing-off by their life-styles. 2. The clothing image according to the 6 clothing styles was different. In the image evaluation of each clothing style by life-style groups, in-active group thought classic style most functional and leisured well-off group, mannish style. Elegant style and dramatic style were estimated positively by the leisured well-off and the appearance showing-off. 3. Selection of clothing style differed according to situations. More formal the situation was, more formal style tended to be selected and for less formal situation, active and mannish style was selected.

• Korean Journal of Human Ecology
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• v.15 no.4
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• pp.585-595
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• 2006

The purpose of this study is to classify the fashion images on sportive style, to find out the difference between the image of sportive style which consumers prefer and the image of sportive style which they want to show and, finally, to analyze their purchase behavior. This research is done with survey method. The subjects of the survey are 835 females in their twenties or their thirties in Pusan area. The data are analyzed with factor analysis, Cronbach's alpha, $X^2$-test, and frequency analysis. The results of this study are as follows: first, sportive style is classified into Sexy, Romantic, Active and Modem image. Second, the results of analysis on consumers' preferring image and their wanting-to-show image to the above-mentioned image classification are as follows: firstly, the subjects' most preferring image and the image which they most want to show is Modem in1age. The second is Sexy image. But the subjects preferred having Modem image. Secondly, consumers' Individuality and apparel's Function are the important reasons to choose the sportive style. Thirdly, Modem image is the most preferred in the images of street wear. Sexy image and Active image are the preferred in the images of sports wear. Third, It is a vivid tone and a dark tone that is the color tone of sportive wear which consumers prefer. They prefer a logo- patterned sports wear, too. The consumers obtain most information on sports wear from sports wear stores. Silhouette is the most decisive design element in consumers' purchasing. The sports wear brands which the subjects prefer are Adidas and Nike.

• International Journal of Advanced Culture Technology
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• v.11 no.3
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• pp.65-77
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• 2023

Chinese martial arts culture is a kind of Chinese kung fu culture, a cultural category that uses martial arts kung fu for chivalry and justice. Chinese martial arts MMORPG online game is the embodiment of Chinese martial arts culture in online games, which is a unique Chinese online game. The image of beggar chivalry is a special chivalrous image in Chinese martial arts culture, and in the top 3 martial arts MMORPG online games, all of them have the image of beggar chivalry, which shows that this image has a wide player base. The Q methodology is an approach that endeavors to discover complex issues in human subjectivity, unlike existing empirical studies. In order to determine the type of beggar chivalry image preference of the game players, 32 beggar chivalry images were selected in the study and three types of beggar chivalry images were found through the Q method: Type 1 is the type of gorgeous and noble beggar chivalry; Type 2 is a competent type and is good at fighting the beggar's chivalry; and Type 3 is comparable relatively refined type. The result of this study is that the image of beggar chivalry preferred by game players is the opposite of the traditional Chinese image of beggar chivalry. The traditional image of beggar is the image of wearing plain and begging in the street, but the image of beggar chivalry that is liked in online games is luxurious, noble, exquisite and about the image of good at fighting. This research result has some value and significance in the development and design of beggar chivalrous image in future martial arts MMORPG online games.

• Journal of Fashion Business
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• v.23 no.3
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• pp.101-114
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• 2019

This study aims to analyze the color characteristics of Japanese cultural product shops and differences in business categories by analyzing the colors of the Gion Shopping Street in Kyoto, Japan. Through the study, the traditional colors are reflected not only in the domestic cultural products but also the interior colors of shops. That way, visitors can be influenced naturally and gain indirect cultural experience to form a good image of Korea, which can help to improve sales of cultural products. The analysis was conducted through the colors of Munsell to determine the overall, dominant, assort, and accent colors based on categories of goods to identify the characteristics of the traditional Japanese cultural product shop. Among the 85 shops that were surveyed, YR and W frequently appeared as chromatic and neutral colors. Dominant was W, and assort was YR. B, P and Y also showed up. In color combination analysis, 35.3% was contrasted. For the hue, 32.9% was dark. Based on goods categories, confectionary shops used YR mainly, while souvenir and fashion accessory shops used W the most. Restaurants mostly had W as thedominant and YR for assorting. Cafes and art shops used Bk the most. The interior colors of cultural products shops should maintain the atmosphere of tradition and convey images of the products well. Based on this research, Korea also needs to actively reflect the interior designs of cultural product shops using traditional colors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1403-1410
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• 2020

Recently, as interest in non-face-to-face experiences and services increases, the demand for web video contents that can be easily consumed using mobile devices such as smartphones or tablets is rapidly increasing. To cope with these requirements, in this paper we propose a technique to efficiently produce video contents that can provide experience of visiting famous places (i.e., stage tour) in animation or movies. To this end, an image dataset was established by collecting images of stage areas using Google Maps and Google Street View APIs. Afterwards, a deep learning-based style transfer method to apply the unique style of animation videos to the collected street view images and generate the video contents from the style-transferred images was presented. Finally, we showed that the proposed method could produce more interesting stage-tour video contents through various experiments.