• 한국의류학회지
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• 제26권7호
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• pp.970-981
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• 2002

The purpose of the study is to clarify orange image in the modern fashion. So kinds of costume sample being visual power in orange have been selected from photographs in fashion magazines and divided into the tones : mist(Vp, Lgr, L), bright(P, B), vivid(S, V, Dp). The study was measured by using 27 semantic differential hi-polar scales. The subjects were 50 female students majoring in clothing and textiles, The data was analyzed using the statistical SPSS package. The data were collected using self-administred questionnaires and analyzed by Cronbach $\alpha$, Factor analysis, MDS, ANOVA Sheff test and Regression analysis. The major findings of this research were as follows. 1. Factor analysis has extracted 4 factors of orange image in the fashion. These factor are Attractiveness, Audacity, Hardness and Softness, Cuteness. 2. There were significant difference in visual evaluation of tones. 3. The discrimination among tones was related to cuteness and weight of orange. 4. The image effect on Preference, Buying needs, Pleasant and Riches was consist of complicated sensibility.

• 한국패션뷰티학회지
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• 제2권3호
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• pp.44-59
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• 2004

The purpose of this study was to find out proper evaluative words according to fashion image and to identify the correlation between clothing style and hair style related to fashion image. The questionnaire was used to collect data. 326 female aged between 23 to 40 were selected for the subjects of this study. The data were analyzed by frequency, factor analysis, pearsons correlation. The results of this study were as follows. 1. Evaluative words for each fashion image were selected by factor analysis. modern image intellectual, cold, urbane, simple, straight. elegance image : graceful, dignified, refined, decorous, luxurious. romantic image : cute, lovely, girlish, feminine, romantic. natural image : natural, comfortable, gentle, intimate, soft. casual image : energetic, active, free, cheerful, vivid. avant-garde image : experimental, strange, creative, avant-garde, irregular. 2. Correlation between clothing style image and hair style image ; clothing style and hair style was positively correlated. with the same image in case of modern, romantic, casual, elegance and avantgarde but natural image of clothing style was correlated with the natural, elegance, romantic, modern image of hair style. 3. The most suitable hair style for the clothing style according to fashion image : The clothing style of a particular image was matched best with the hair style of the same image.

• 정보처리학회논문지B
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• 제11B권4호
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• pp.403-410
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• 2004

최근 의료 영상 분석(Medical Image Analysis)이나 영상 검색(Image Retrieval)을 위한 전처리(Preprocessing) 단계로 영상 분석(Image Analysis)에 대한 연구가 활발히 진행되고 있다. 본 논문에서는 영상 검색에서 색상 성분(Color Component)의 활용 방법을 제안하고자 한다. 이미지를 검색하기 위해 색상 성분을 기반으로 하고, 색상(Color)을 분석하기 위한 기법으로 CLCM(Color Level Co-occurrence Matrix)과 통계적 기법을 이용하고 있다. CLCM은 기하학적 회전 변환(Geometric Rotate Transform)을 통해서 색상 성분을 3차원 공간상에 투영(Projection)하여 공간 관계(Spatial Relationship)로부터 나타나는 분포를 해석하는 방법으로, 본 논문에서 제안하는 주제이다. CLCM은 색상 모델에서 만들어지는 2차원 히스토그램을 지칭하며 색상 모델의 기하학적인 회전 변환을 통해서 생성된다. 그리고 이를 분석하기 위한 방법으로 통계 기법을 활용하고 있다. CLCM과 유사하게 2차원 분포도를 사용하는 GLCM(Gray Level Co-occurrence Matrix)[1]과 불변 모멘트(Invariant Moment)[2,3] 같은 알고리즘은 2차원적인 데이터를 해석하기 위하여 기본적인 통계 기법을 활용하고 있다. 하지만 GLCM과 불변 모멘트가 각각의 도메인에 최적화되어 있다 하더라도 공간 좌표상에 존재하는 불규칙적인 데이터를 완전히 해석할 수는 없다. 즉 GLCM과 불변 모멘트는 기초 통계 기법만을 사용하고 있기 때문에 추출된 특징들의 신뢰성이 낮다는 것이다. 본 논문에서는 이러한 단점을 보완하여 공간 관계를 해석함과 동시에 데이터의 가중치를 해석하기 위해 전형적인 다변량 통계에서 사용하는 주성분 분석(Principal Component Analysis)[4,5]을 이용하고 있다. 그리고 데이터의 정확도를 높이기 위해서 3차원 공간상에 색상 성분을 투영하여 이를 회전시키면서 데이터의 특성을 다각도에서 추출하는 방법을 제시한다.

• Spatial Information Research
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• 제10권2호
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• pp.331-343
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• 2002

최근 위성 영상정보를 이용하는 활용 연구의 중요성이 강조되면서 다중 분해능을 갖는 위성 영상정보의 통합적인 적용에 대한 관심이 증가하고 있다. 본 연구에서는 광역적인 분석에서 다중 분해능 위성 영상정보의 광역적 통합 분석에 대한 적용 가능성을 살펴보기 위하여 경기도 남양주시에 대한 Landsat 7 ETM+ 다중 분광 영상정보와 KOMPSAT EOC 영상정보에 대한 화소 값(DN) 분석 및 다중 분해능 영상 분류를 수행한 뒤에, 분류 결과를 같은 지역에 대하여 구축된 토지이용현황 주제도와 대비 분석하고자 하였다. 다중 분해능 영상 분류로 나타난 주요 결과로는 단일 분해능 영상정보 분류결과에 비하여 도로 정보와 같은 선형적인 요소의 추출이 용이한 것으로 나타났다. 한편 연구 지역내 주요 도로에 대한 영향권 설정 분석 또는 거리 질의 방법을 이용하여 수행된 영상 분류 결과와 토지이용현황 주제정보의 대비 분석 결과는 두 가지 정보가 유사한 패턴을 보이므로, 다중 분해능 영상정보의 분류 결과는 도시 환경분석문제에도 효과적으로 이용될 수 있을 것으로 생각된다.

• 한국의상디자인학회지
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• 제20권1호
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• pp.79-91
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• 2018

The objective of this study was to determine the influence of women's attitudes toward women's underwear on image-creation efficacy and appearance management attitude. A total of 405 surveys of women working at an industrial complex in the Daegu-Kyoungbuk area were used for data analysis. Frequency, factor analysis, reliability analysis, and regression analysis were used for data analysis. The findings were as follows. The sub-factors of women's attitudes regarding women's underwear were found to be 'aesthetics/body-style compensation', 'ostentation', 'functionality', and 'manner estimation' and the sub-factors of image-making efficacy were 'display confidence', 'face-image confidence' and 'display ability'. Appearance management attitude had factors such as total coordination, weight management, skin management, and pursuit of change. Aesthetics/body-style compensation, functionality, and ostentation, which were sub-variables of attitudes toward underwear, had a significant influence on image-creation efficacy. Aesthetics/body-style compensation and ostentation had significant influences on appearance management attitude. Aesthetics/body-style compensation was found to have a significant influence on all sub-variables of both image-creation efficacy and appearance management attitude.

• 한국의류학회지
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• 제24권5호
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• pp.748-759
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• 2000

The purposes of this study were 1) to classify wearing situation of male adolescents and 2) to classify self-image and CPI(Clothing Preference Image) of male adolescents and 3) to segment consumer group by self-image and to find the differences in self-image and CPI by situation among groups. For the data collection a questionnaire was distributed to male adolescents who were residents in Seoul and Taejeon. The statistics used for the data analysis were factor analysis, multiple dimensional scale, mean, percentage, peason-correlation, cluster analysis, one-way ANOVA, Duncan-test by the SPSSWIN program. The results of this study are as follows: 1) The self-image of male adolecents is categorized by seven factors; sophisticate and fashion conscious, active, practical and realistic, flank and pure, young-looking, feminine, and slender. Based on seven factors, the consumer group is categorized to five groups; practical and realistic Group1, young-looking and feminine Group2, characterless Group3, active Group4, sophisticate and flank Group5. 2) Wearing situations are divided into three categories; in downtown, in urban, at festival. In downtown, CPI are divided into six elements; ornamental, simplex, sexy, feminine, neat, young, and sophisticate. In urban, CPI are divided into five elements; ornamental, simple, sexy, feminine, young-looking, and sophisticate. At festival, CPI are divided into four elements; unique, simple, feminine, and formal. To conclude, the male adolescent consumers are categorized by self-image, and the different CPIs are sought by different wearing situations.

• 한국콘텐츠학회논문지
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• 제17권7호
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• pp.443-454
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• 2017

온라인에서 생성되는 관광지 관련 정보들을 활용한 관광지 이미지 분석은 관광소비자들의 관광목적지에 대한 인식을 설명할 수 있는 유의미한 정보를 도출할 수 있으며, 관광소비자들의 특정 관광지에 대한 이미지를 더욱 심층적으로 이해할 수 있다. 본 연구는 온라인 빅데이터를 활용한 대구의 관광지 이미지 실례연구를 실시하여 대구의 관광지 이미지를 분석하고 시사점을 도출하고자 하였다. 국내 포털 사이트를 대상으로 텍스트 마이닝과 사회연결망 분석을 실시하여, 대구의 관광지 이미지를 형성하는 관광지 이미지 요소들을 추출하고 영향 정도를 분석하였다. 연구 결과에 따르면 관광객 인프라시설과 문화와 예술, 역사 등의 관광지 이미지 형성 요소들이 대구의 관광지 이미지를 형성하는 주요한 요소들로 파악되었으며, 특히, '대구중구골목투어'가 전체적인 대구의 관광지 이미지 형성에 핵심적인 역할을 하는 것으로 파악되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권2호
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• 한국환경과학회:학술대회논문집
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• 한국환경과학회 2003년도 International Symposium on Clean Environment
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• pp.33-36
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• 2003

Principal component analysis (PCA) was used to improve image classification by the unsupervised classification techniques, the K-means. To do this, I selected a Landsat TM scene of Jeju Island, Korea and proposed two methods for PCA： unstandardized PCA (UPCA) and standardized PCA (SPCA). The estimated accuracy of the image classification of Jeju area was computed by error matrix. The error matrix was derived from three unsupervised classification methods. Error matrices indicated that classifications done on the first three principal components for UPCA and SPCA of the scene were more accurate than those done on the seven bands of TM data and that also the results of UPCA and SPCA were better than those of the raw Landsat TM data. The classification of TM data by the K-means algorithm was particularly poor at distinguishing different land covers on the island. From the classification results, we also found that the principal component based classifications had characteristics independent of the unsupervised techniques (numerical algorithms) while the TM data based classifications were very dependent upon the techniques. This means that PCA data has uniform characteristics for image classification that are less affected by choice of classification scheme. In the results, we also found that UPCA results are better than SPCA since UPCA has wider range of digital number of an image.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1390-1393
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• 1987

This paper describes the image data compression based on the image decomposition. We reduced the processing time using the segmentation based on the distribution of grey level, and obtained high compression rate using the Huffman run-length coding for the segmented image, and the 2-Dimensional least square curve fitting and the shift coder for each region.