• 대한방사선기술학회지:방사선기술과학
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• 제42권2호
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• pp.119-130
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• 2019

Positron emission tomography (PET) is widely used imaging modality for effective and accurate functional testing and medical diagnosis using radioactive isotopes. However, PET has difficulties in acquiring images with high image quality due to constraints such as the amount of radioactive isotopes injected into the patient, the detection time, the characteristics of the detector, and the patient's motion. In order to overcome this problem, we have succeeded to improve the image quality by using the dynamic image reconstruction method based on singular value decomposition. However, there is still some question about the characteristics of the proposed technique. In this study, the characteristics of reconstruction method based on singular value decomposition was estimated over computational simulation. As a result, we confirmed that the singular value decomposition based reconstruction technique distinguishes the images well when the signal - to - noise ratio of the input image is more than 20 decibels and the feature vector angle is more than 60 degrees. In addition, the proposed methode to estimate the characteristics of reconstruction technique can be applied to other spatio-temporal feature based dynamic image reconstruction techniques. The deduced conclusion of this study can be useful guideline to apply medical image into SVD based dynamic image reconstruction technique to improve the accuracy of medical diagnosis.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.34-46
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• 2019

A method for detecting foreign substances in mould based on scatter grams was presented to protect moulds automatically during moulding production. This paper proposes a wavelet transform foreign detection method based on Monte Carlo analysis mechanism to identify foreign objects in the tube. We use the Monte Carlo method to evaluate the image, and obtain the width of the confidence interval by the deviation statistical gray histogram to divide the image type. In order to stabilize the performance of the algorithm, the high-frequency image and the low-frequency image are respectively drawn. By analyzing the position distribution of the pixel gray in the two images, the suspected foreign object region is obtained. The experiments demonstrate the effectiveness of our approach by evaluating the labeled data.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2021년도 봄 학술논문 발표대회
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• pp.162-163
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• 2021

Currently, the depth of cracks is measured using ultrasonic detectors in maintenance practice. This method consists of measuring the depth of cracks by attaching ultrasonic depth measuring equipment to the concrete surface, and there are restrictions on the timing and location of the inspection. These limitations can be addressed through the development of image-based crack depth measurement AI technology. If crack depth measurements are made based on images, restrictions on the timing and location of inspections can be lifted because images acquired with simple filming equipment can be used as input information. To efficiently develop these artificial intelligence technologies, it is essential to identify the interrelationship between crack depth measurements and image characteristic information. Thus, this study is a basic study of the development of image-based crack depth measurement AI technology and aims to identify image characteristic information related to crack depth.

• 패션비즈니스
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• 제21권3호
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• pp.29-42
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• 2017

This study investigates the impact of female consumer self-image on pursued fashion style. A survey was carried out among 717 women between the ages of 20 and 60 living in Seoul, Incheon, and Gyeonggi. Analysis was conducted in the following manner: SPSS 18.0 was used to perform an Exploratory Factor Analysis (descriptive analysis, principal factor analysis, Pearson correlation analysis, frequency analysis, and reliability analysis) and AMOS 21.0 was used to carry out a Confirmatory Factor Analysis. Based on a Structure Equation Model, results show that ideal self-image and realistic self-image, which are factors derived from psychology, affect pursued fashion style. By contrast, social self-image - derived from social contexts - does not. Therefore, the female consumers' self-image influences pursued fashion style; this is opposed to the relationship between the realistic self-image and ideal self-image of women, which is more unconscious and self-satisfying. The presented results indicate that we should respond to changes in the fashion industry and develop a deeper understanding of consumer niches to discover the factors that predict purchasing behavior. This knowledge can then be applied to establish market strategies. This study contributes to the literature by producing preliminary data that can help support such strategy formulation in the fashion and clothing industry.

• 복식문화연구
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• 제19권3호
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• pp.622-636
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• 2011

The purpose of this study was to assess the effects of self-image congruence on attitudes toward purchase intentions of children's clothing via the Behavioral Intention Model. The empirical study was conducted via on-line survey and data were collected from mothers with children aged 6 to 10 years. A total of 593 respondents answered the questionnaire and 574 usable data were statistically analyzed. SPSS 18.0 was used to conduct descriptive statistical analysis, factor analysis, reliability analysis, cluster analysis, Chi-square test, ANOVA, and multiple regressions. A K-means cluster analysis was conducted based on three dimensions brand images of children's wear. Respondents were divided into four groups: elegant image group, multiple image group, ordinary image group, and childlike image group. Characteristics of consumers and clothing evaluative criteria that mothers considered important differed significantly across groups. Moreover, based on these groups, each dimension of self-congruence had different effects on brand attitude. Brand attitude and subjective norms had different effects on purchase intentions. In conclusion, levels of self-congruence and factors influencing purchase intention varied according to brand images of children's wear.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 1998년도 추계학술발표 논문집
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• pp.273-277
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• 1998

The purpose of this study was to develop a large representative data base for jeans marketing strategy. This study was to survey brand awareness and analyze brand image and consumer's seeking image. The 700 questionnaires were distributed and 656 reliable ones were used for statistical analysis. A SAS statistical package including frequency table, factor analysis, analysis of variance, Duncan's multiple range test, Peason's correlation test was used. The results are as follows: 1. Brand awareness involves "brand recall" based on asking a person to name recalled first, and "brand recognition" based on asking to identify brand name from 30 given brands. The result indicated that "Levi" was dominant for brand recall and Guess was dominant for brand recognition. 2. Regarding the brand image, the result showed that "Vov" was best represented for sophisticated 8t trendy brand images, "Storm" for sophisticated brand image, "Jambangee" for reasonable price & comfortable brand images, and "Levis" for classic & design/color brand images. 3. As a result of factor analysis on consumer's seeking image, six factors(characteristic/gay, intelligent/sexy, feminine/sophisticated, active/functional, cute/young, simple/comfortable) were found. Several factors had a relationship with demographic variables, preferred design, fashion interest.

• 한국통신학회논문지
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• 제27권4A호
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• pp.345-355
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• 2002

본 논문에서는 지문영상의 향상을 위한 방향성 필터링의 새로운 기법을 제안한다. 지문영상은 융선의 규칙적인 열의 방향성 맵으로 구성되어 있으며, 융선의 주방향성은 지문영상의 주요 특징점을 추출하기 위한 융선의 연결이나 잡음의 제거 등 지문영상의 전처리과정에 매우 중요하다. 방향성대역 통과 필터뱅크(Directional Filter Bank ; FB)는 입력영상을 주파수의 성분이 아닌 방향성 성분으로 분해한 다음, 이 대역영상으로부터 원영상을 완전하게 복원하는 필터이다. 본 논문은 DFB를 이용하여 지문영상을 방향성 대역 영상으로 분해하여 이를 처리한 후 복원함으로써 지문영상을 향상시키는 알고리듬을 제안한다.

• Ocean Systems Engineering
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• 제2권2호
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• pp.137-145
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• 2012

Recent developments in underwater image recognition methods have received large attention by the ocean engineering researchers. In this paper, an improved bi-dimensional empirical mode decomposition (BEMD) approach is employed to decompose the given underwater image into intrinsic mode functions (IMFs) and residual. We developed a joint algorithm based on BEMD and Canny operator to extract multi-pixel edge features at multiple scales in IMFs sub-images. So the multiple pixel edge extraction is an advantage of our approach; the other contribution of this method is the realization of the bi-dimensional sifting process, which is realized utilizing regional-based operators to detect local extreme points and constructing radial basis function for curve surface interpolation. The performance of the multi-pixel edge extraction algorithm for processing underwater image is demonstrated in the contrast experiment with both the proposed method and the phase congruency edge detection.

• 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).

• 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.