• Journal of Broadcast Engineering
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• v.23 no.3
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• pp.345-350
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• 2018

CNN (convolution neural network) has become the most popular artificial intelligence technique and shows remarkable performance in image classification task. In this paper, we propose a CNN-based classification method for various street images as well as a method of evaluating the safety score for the street. The proposed method consists of learning four types of street images using CNN and classifying input street images using the learned CNN model followed by evaluating the safety score. During the learning process, four types of street images are collected and augmented, and then CNN learning is performed. It is shown that learned CNN model classifies input images correctly and the safety scores are evaluated quantitatively by combining the probabilities of different street types.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.242-244
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• 2004

In this paper, we design and implement an integrated management system for geographic data and vehicular images using a Geographic Information System (GIS). Integrated management of geographic data and vehicular images is very important to manage and to provide them to users effectively because of a large volume of vehicular images. To manipulate these data together, we consider a vehicular image as a polygon which is a type of popular geographic data types. The polygon represents a region in which spatial objects appear the vehicular image.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.225-228
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• 2003

The purpose of this paper is to develop a semi -automated system for medical image processing with which tissues or organs from medical images can be segmented and classified by people who have basic knowledge of image processing. In addition, the proposed medical image processing system is independent on types of human tissues or images. In this paper, a new semi-automated image processing system with essential image processing functions for medical images is introduced

• The Research Journal of the Costume Culture
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• v.22 no.6
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• pp.955-964
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• 2014

This study is to explore the relationships between sensibility images of mannequins and apparel shops. A total of 113 consumers participated in experiments with photo stimuli of 2 mannequin types (realistic and semi-abstract mannequins) and 4 brand shops of women's casual wear. In results, luxurious, chic, strong, sexy, and young images were more strongly perceived from realistic mannequins than semi-abstract mannequins whereas simple and soft images were more strongly perceived from semi-abstract mannequins than realistic mannequins. Shops using realistic mannequins indicated strong images whereas shops using semi-abstract mannequins presented soft, comfortable, and feminine images. In the correlation analysis, luxurious, chic, strong, and young images of realistic mannequins were consistent with shop images using realistic mannequins. Also, luxurious, chic, soft, comfortable, and feminine images of semi-abstract mannequins were consistent with shop images using semi-abstract mannequins. In order to clearly communicate brand concepts with consumers, mannequin that is a key element of visual merchandising in the apparel shop, should be carefully selected, considering the accordance with shop image.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.9-17
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• 2007

In this study, Digital Elevation Models (DEMs) were constructed from color images, grayscale images and each bands (Red, Green, Blue) of color image, and the accuracies of each DEMs were evaluated, And then, correlation coefficients between left and right images of each stereopairs were analyzed. The DEM can be constructed conventionally from the digital map and stereopair images using image matching. The image matching requires stereo satellite images or aerial photographs. In case of rotor aerial photographs, these are to be scanned in 3 bands (Red, Green, Blue). For this study, 5 types of images were acquired; color, grayscale, RED band, GREEN band, and BLUE band image. DEMs were constructed from 5 types of stereopair images and evaluated using elevation points of digital maps. In order to analyze the cause of various accuracies of each DEMs, the similarity between left and right images of each stereopairs were analyzed. Consequently, the accuracy of the DEM constructed from RED band images of color aerial photograph were proved best.

• Journal of the Korea Fashion and Costume Design Association
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• v.16 no.4
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• pp.37-47
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• 2014

Recently, the Korean wave has caused many foreign nations to pay attention to various fields including Korean culture, arts, fashion and beauty. Korean images so far were mainly discussed in terms of aesthetics, and there is lack of efforts to visually shape Korean images. On the contrary, Oriental images focused around Japan and China have greater global influence and better globalized fashion styles compared to Korea. Therefore, it is necessary to suggest a globalized proposal of Korean fashion styling which reflects original and unique aesthetic characteristics of Korea that can be accepted by the global market. In this study, the concept and types of fashion styling and stylists were examined, as well as the definition of Korean image and types and characteristics of Korean images shown on fashion magazines. Also, after collecting photographs having Korean images that were included in the editorial fashion styling of Vogue Korea, their characteristics were analyzed. The results of interviews with professional fashion stylists were summarized to propose an editorial fashion styling with Korean image for overseas consumers who long for and wish to imitate Korean fashion styling.

• Journal of the Korean Society of Clothing and Textiles
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• v.46 no.2
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• pp.321-330
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• 2022

This study analyzed the difference between 2D image and 3D virtual clothing images based on stripe arrangement to obtain fundamental data for slim appearance. First, the slimming effect according to the three types of stripe ratio was examined. Subsequently, the slimming effects of seven types of one-piece dress designs according to the stripe location were analyzed. Subjective ranking was evaluated. The width items and radius of curvature were measured for the image's respective parts. Consequently, in 2D image and 3D virtual clothing images, the one with the narrowest stripe ratio was evaluated as the slimmest; however, the conditions for the slimming effect were different. In the seven one-piece dress designs, a difference was apparent in the ranking of the 2D image and 3D virtual clothing images. In the 3D virtual clothing image, arranging the stripes on the entire garment proved inefficient. The stripes were curved according to the curvature of the human body, creating an optical illusion that differed from that of the 2D image.

• Smart Structures and Systems
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• v.30 no.4
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• pp.385-395
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• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• Journal of the Korean Society of Costume
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• v.64 no.5
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• pp.91-107
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• 2014

This purpose of this study is to analyze the pursuit of current fashion trends and fashion image types of middle-aged women in Korea. This study attempted to investigate the standards and properties of these different types of fashion images. The overall characteristics of middle-aged women and the concepts of personal image and fashion image were investigated through literature research. Survey and analysis based on Q methodology was conducted. Factors of personal image, fashion image and components of fashion image were analyzed by collecting information from in-depth workshops and focus group interview of an expert group. The results of this study are as follows: 1) The main factors influencing the current fashion image of women in their forties and fifties in Korea are classified into six types. 2) The elements of fashion image that Korean women in their 40s and 50s pursue are divide into four types. 3) Each type can be recognized by specific fashion image components and colors. 4) This shows that middle-aged Korean women are highly conscious of how others perceive them and have a desire to not stand out from others. It also shows that they are very active in pursuing fashion and following trends, which is the image of an active and dignified woman. This study provides the framework that enables sorting of the fashion images types that middle-aged Korean women want to pursue. The results from analyzing the factors can be used to recognize specific fashion images, and can be used in the planning and designing of fashion items for middle-aged Korean women.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.99-108
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• 2007

In this paper, we design and implement the Film Defect Inspection System (FDIS) that detects film defects and determines their types which can be used for producing polarized films of TFT-LCD. The proposed system is designed to detect film defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects. To determine defect types, we extract features such as shape and texture of defects, and compare those features with corresponding features of referential images stored in a template database. Experimental results using FDIS show that the proposed system detects all defects of test images effectively (Precision 1.0, Recall 1.0) and efficiently (within 0.64 second in average), and achieves the considerably high correctness in determining defect types (Precision 0.96 and Recall 0.95 in average). In addition, our system shows the high robustness for rotated transformation of images, achieving Precision 0.95 and Recall 0.89 in average.