• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.80-90
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• 1982

This study was undertaken to determine the influence of a chronic disease on body image, and to show that body image is directly related to one's self concept. Body image is the concept of one's own body based on present and past perception, and is elated to one's self concept. Body image is a dynamic concept constantly changing throughout the life cycle but it changes greatly in illness, surgery, and accident. The child with a chronic disease experiences pain and immobilization due to illness and he/she experiences a strange environment in the hospital. illness often brings feeling of frustration and loss of self-esteem. Therefore this study was done to compare the body image of a child with a chronic disease（Asthma), with that of a normal healthy child, and to determine the relationship between the body image and self concept. The subjects in this study were 36 children being treated for asthma at the allergy clinic of Y University Hospital in Seoul (patient group) and 44 children attending elementary school in Kwanak Ku Seoul (normal healthy group). For the measurement of the body image, the researcher used Secord & Jourard's Body Cathexis Scale, and another scale which was constructed after reading about Osgood's Semantic Differential Method. For the measurement of the self concept, the researcher used Jacox & Stewart's Health Self Concept. The period for data collection was from October 7th to October 27th, 1982. The analysis of data was done by use of Percentage, t-test, Chi-square test, Pearson Correlation Coefficient and ANOVA, The results of the study were as follows: 1. The first hypothesis,“That the chronically ill (Asthma) child will have a more negative body image than the normal healthy child.”was supported. 2. The second hypothesis,“The more negative the body image, the lower the self concept.”was also supported. 3. The researcher failed to obtain statistically significant results in the analysis of the general characteristics which affect the body image except in the case of the older child as compare to the younger Child having a mole positive body image (r=.2751, r=.2481, p<.05). However it was found that, 1) Boy's have a more positive body image than girls (Mean=〔37.81, 141.09〕,〔37.00, 126.54〕), 2) The child who has been hospitalized has a more negative body image than the child who has never been hospitalized (Mean=〔33.25, 122.45〕,〔35.68, 129.93〕). 3) The younger the child when the disease is discovered and diagnosed, the more negative the body image (Onset of illness: Mean=〔31.44, 117.33〕,〔34.00, 103.50〕, 〔35.75, 140.38〕,〔36.33, 130.00〕, Time of Diagnosis: Mean=〔29.00, 117.33〕,〔33.89, 115.00〕,〔33.36, 124.93〕,〔37.10, 139. 20〕). In conclusion the chronically ill(.Asthma) child has a more negative body image than the normal healthy child, and the more negative the body image the lower the self concept. Therefore the concept of body image is useful in understanding the influences of chronic disease on body' image and self concept.

• Journal of Korea Multimedia Society
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• v.15 no.5
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• pp.569-576
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• 2012

This paper presents an x-ray medical image panorama system which can overcome the smallness of the images that exist on a source computer during remote medical processing. In the system, after the standard medical image format DICOM is converted to the PC standard image format, a MSR algorithm is used to enhance X-ray images of low quality. Then SURF and Multi-band blending are applied to generate a panoramic image. Also, this paper evaluates the proposed SURF based system through the average gray value error and image quality criterion with X-ray image data by comparing with a SIFT based system. The results show that the proposed system is superior to SIFT based system in image quality.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4835-4848
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• 2020

The recent expansion of the digital content market is increasing the technical demand for various facial image transformations within the virtual environment. The recent image translation technology enables changes between various domains. However, current image-to-image translation techniques do not provide stable performance through unsupervised learning, especially for shape learning in the face transition field. This is because the face is a highly sensitive feature, and the quality of the resulting image is significantly affected, especially if the transitions in the eyes, nose, and mouth are not effectively performed. We herein propose a new unsupervised method that can transform an in-wild face image into another face style through radical transformation. Specifically, the proposed method applies two face-specific feature loss functions for a generative adversarial network. The proposed technique shows that stable domain conversion to other domains is possible while maintaining the image characteristics in the eyes, nose, and mouth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.2192-2200
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• 2015

In this paper, we propose an efficient vehicle image compensation algorithm based on Histogram Equalization. The proposed a vehicle image compensation algorithm was elimination to the vehicle image shake using motion compensation and motion estimation. And, algorithm was calculated the histogram of pixel values from each sub-image by dividing the image as the constant size areas in order to image enhancement. Also, it had enhancement to the image by adjusting the gradient. The proposed algorithm was evaluate the difference between of performance and time, image by applied to the IP, and were confirmed the image enhancement with removing of vehicle camera image shake. In this paper, the proposed vehicle image enhancement algorithm was demonstrated effectiveness when compared to existing vehicle image stabilization, because the elimination of shake for the vehicle images used real-time processing without using a memory. And it was obtained the reduction effect of the computation time by the calculated through block matching, and obtained the better restoration result for naturalness of the image with the lowest noise.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.291-298
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• 2022

A generative adversarial network (GAN) is a network in which two internal neural networks (generative network and discriminant network) learn while competing with each other. The generator creates an image close to reality, and the delimiter is programmed to better discriminate the image of the constructor. This technology is being used in various ways to create, transform, and restore the entire image X into another image Y. This paper describes a method that can be forged into another object naturally, after extracting only a partial image from the original image. First, a new image is created through the previously trained DCGAN model, after extracting only a partial image from the original image. The original image goes through a process of naturally combining with, after re-styling it to match the texture and size of the original image using the overall style transfer technique. Through this study, the user can naturally add/transform the desired object image to a specific part of the original image, so it can be used as another field of application for creating fake images.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.175-175
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• 2003

• Journal of Fashion Business
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• v.20 no.2
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• pp.97-106
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• 2016

This study aims examine specific image preferences through selected fashion style and evaluation terms. Therefore, this study provided basic information in order to help select a suitable fashion style within a desired image. As times are changing, most people obtain information about fashion through the use of the Internet by utilizing mobile phones and personal computers and less through magazines, newspapers and so on. When people buy clothes, they seem to be more influenced by the design and color than by price and quality. It is not perceived important for buying like company, advertising, the place of purchase etc. The results of the analysis reveal the structural elements of an image having a sense of fashion style and can be categorized as a natural image, romantic image, elegance image, casual image and avant-garde image. Among the six fashion styles, the natural image is regarded the highest A style. The romantic image is the highest B style and elegance image is the C, D, E style. The casual image is the highest F style and avant-garde image is a D style. In regards to appropriate fashion style by ages, the twenty-something population is investigated in finding a fit that is lively and vibrant in style while the thirty-something population is perceived to identify with a more feminine style. The forty-something population has shown to prefer a more luxurious style. Through this observation one can see that there is a distinction is fashion style between ages.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.9-16
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• 2008

In this paper, we present an algorithm for synthesizing intermediate view image from a stereoscopic pair of images. An image of multiview is need for people in order to easily recognize 3D image. However, if many cameras are use for that, not only does system get more complicated but also transmission rating cause a big trouble. Hence, stereo images are photograph and issue on the sending side and algorithm to generate several intermediate view image is able to be use on the receiving side. The proposed method is based on disparity space image. First of all, disparity space image that is depicted by the gap of pixel followed by disparity of stereo image is generated. Disparity map is made by utilizing disparity space image for searching for optimal disparity path then eventual intermediate view image is generated after occlusion region which does not match is processed. Experimental results illustrate the performance of the proposed technique and we obtained a high quality image of more than 30 dB PSNR.

• Journal of Intelligence and Information Systems
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• v.13 no.4
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• pp.79-90
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• 2007

The image quality measure is an important issue in the image processing. Several methods which measure the image quality have been proposed and these are based on the mathematical point of view. However, there is difference between the mathematicalmeasure and the measure based on the human visual system and a new measure has to be proposed because the final target of the image is a human visual system In this paper, a statistical image quality measure which is considered the human visual feature was suggested. The human visual system is using the global quality of the image and the local quality of the image and the local quality is more important to human visual system. In this paper, the image divided into several segments and the image qualities were calculated respectively. After then, the statistical method using scoring was applied to the image qualities. The result of the image quality measure was similar to the result of measure based on the human visual system.

• Archives of design research
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• v.14 no.4
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• pp.77-85
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• 2001

In order to sustain competitive advantage through strategic product differentiation in global market, this research aims to establishment of strategic design conditions for domestic manufacturing firms through the means of product image alignment and positioning, using visual character of product as the foundation for establishing corporate image. Case studies on how globally successful multinational companies manage design strategy and corporate culture uniformly for coherent product image management are reviewed. To establish product image alignment methodology for manufacturing firms, the criteria for product image positioning and aligned product image development process are developed in three stages. First, in image positioning stage, it is important to set the direction for corporate image and design strategy by identifying consumer needs through market trends analysis and forecasting as well as comparative positioning of the analysis results of the firms's own corporate image and that of the competitor's. Second, in awareness building stage, before building the product image alignment, firm-wide shared awareness of differentiated corporate and product image, establishment of designer's role as the consolidated manager of product image, sharing form language, and building the infrastructure for defining form information should be preceded. Third, in managing the aligned product image stage, the condition for setting up image information management guideline, management of decision making process for arriving at logical decisions on product form, development of guideline for increasing efficiency and its evaluation system, and co-development of related design should be induced. Further research on the product image alignment with corporate image should be conducted through various empirical studies on image and form, building an information system of related design language, and continuous assessment and management of applied solutions.