• Journal of the Korean Society of Clothing and Textiles
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• v.24 no.7
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• pp.953-963
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• 2000

This study intended to analyse the factors of brand image and brand image positioning of domestic 20’s female apparel(formal wear) among the consumer groups segmented by benefits sought in apparel and to provide marketing strategy of brand image. The subject of this study were 605 working women in their 20’s living in seoul, and the model sampling was done by convenienced sampling method based on the subjects age and occupation. Survey based on references and former studies was used. and statistical methods such as frequency, percentage, mean, factor analysis, preference regression were applied. The results of this study were as follows. 1. The factor structures of brand image were classified into symbolism/aesthetics, and practicality. 2. Perception, ideal preference vector, and brand preference of brand image were proven to be significantly different among the four segmented consumer groups.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1170-1178
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• 2021

This paper proposed a versatile algorithm based on a dual-tree complex wavelet transform for intensifying the visual aspect of medical images. First, the decomposition of the input image into a high sub-band and low-sub-band image is done. Further, to improve the resolution of the resulting image, the high sub-band image is interpolated using Lanczos interpolation. Also, contrast enhancement is performed by singular value decomposition (SVD). Finally, the image reconstruction is achieved by using an inverse wavelet transform. Then, the Gaussian filter will improve the visual quality of the image. We have collected images from the hospital and the internet for quantitative and qualitative analysis. These images act as a reference image for comparing the effectiveness of the proposed algorithm with the existing state-of-the-art. We have divided the proposed algorithm into several stages: preprocessing, contrast enhancement, resolution enhancement, and visual quality enhancement. Both analyses show the proposed algorithm's effectiveness compared to existing methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1341-1349
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• 2011

This research is focused on an analysis on the structure of RAW image file and the development of a RAW image file viewer for an embedded device. Recently, several RAW image file formats are being used for saving and displaying the images created by various DSLR cameras, and the necessity of handing RAW images in mobile multimedia devices is increasing. For the development of RAW image decoding/encoding library applicable to WinCE-based embedded devices viewer, an analysis of RAW image file formats, such as CRW, CR2, PEF, NEF, MRW, have been performed because their formats are not released in public. By using the library, the analysis software which can extract RAW image data, 2~3 JPEG image files and other informations such as the specification of a camera and various photographic parameters from RAW image files, were developped and a RAW image file viewer which can run in WinCE-based embedded devices. The experimental result has shown that the viewer could encode and decode RAW image files successfully and it took approximately 10secs to load them to the screen in S3C6410 based embedded platform. The outcomes of this research cloud be a good information and solution to multimedia application developers.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.209-214
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• 2010

In this paper, We analyzes the image along the platform (Open Eye), through prevention of dementia or stroke patients and cognitive rehabilitation for the proposed system. This way through the camera image according to user's movement gained OpenCV image processing library, which is based on motion analysis, a part of this rehabilitation is to apply to cognitive rehabilitation. Therefore, this paper proposes a new image analysis system has been exposed to the elderly or stroke patients with dementia, their hand gestures through which patients can detect the image of the cognitive rehabilitation to help them in the analysis of the image analysis system is proposed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2511-2519
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• 2009

In this paper, we introduce the methodological system design via feature selection using Principal Component Analysis and Particle Swarm Optimization algorithms. The overall methodological system design comes from three kinds of modules such as preprocessing module, feature extraction module, and recognition module. First, Histogram equalization enhance the quality of image by exploiting contrast effect based on the normalized function generated from histogram distribution values of 2D face image. Secondly, PCA extracts feature vectors to be used for face recognition by using eigenvalues and eigenvectors obtained from covariance matrix. Finally the feature selection for face recognition among the entire feature vectors is considered by means of the Particle Swarm Optimization. The optimized Polynomial-based Radial Basis Function Neural Networks are used to evaluate the face recognition performance. This study shows that the proposed methodological system design is effective to the analysis of preferred face recognition.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.467-474
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• 2024

Monitoring and managing the condition of underground utilities is crucial for ground stability. This study aims to determine whether images obtained using ground penetrating radar (GPR) accurately reflect the characteristics of buried pipelines through image analysis. The investigation focuses on pipelines made from different materials, namely concrete and steel, with concrete pipes tested under various diameters to assess detectability under differing conditions. A total of 400 images are acquired at locations with pipelines, and for comparison, an additional 100 data points are collected from areas without pipelines. The study employs GPR at frequencies of 200 MHz and 600 MHz, and image analysis is performed using machine learning-based convolutional neural network (CNN) techniques. The analysis results demonstrate high classification reliability based on the training data, especially in distinguishing between pipes of the same material but of different diameters. The findings suggest that the integration of GPR and CNN algorithms can offer satisfactory performance in exploring the ground's interior characteristics.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.10
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• pp.11-18
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• 2018

In this study, the cross-sectional image has been acquired to evaluate the aggregate distribution affecting quality of lightweight aggregate concrete, and through the binarization method, the study is to calculate the aggregate area of upper and lower sections to develop the method to assess the aggregate distribution of concrete. The acquisition of cross-section image of concrete for the above was available from the cross-sectional photography of cleavage tension of a normal test specimen, and an easily accessible and convenient image analysis software was used for image analysis. As a result, through such image analyses, the proportion of aggregate distribution of upper and lower sections of the test specien could be calculated, and the proportion of aggregate area U/L value of the upper and lower regions of concrete cross-section was calculated, revealing that it could be used as the comprehensive index of aggregate distribution. Moreover, through such method, relatively easy image acquisition methods and analytic methods have been proposed, and this indicated that the development of modeling to assess aggregate distribution quantitatively is available. Based on these methods, it is expected that the extraction of fundamental data to reconsider the connectivity with processes in concrete will be available through quality assessment of quantitative concrete.

• Spatial Information Research
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• v.19 no.6
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• pp.43-54
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• 2011

This study proposes a rule-based image classification method for the time-series analysis of changes in the land surface of the Seongnam-Yongin area using satellite-image data from 2000 to 2009. In order to identify the change patterns during each period, 11 classes were employed in accordance with statistical/mathematic rules. A generalized algorithm was used so that the rules could be applied to the unsupervised-classification method that does not establish any training sites. The results showed that the urban area of the object increased by 145% due to housing-site development. The image data from 2009 had a classification accuracy of 98%. For method verification, the results were compared to land-cover changes through Post-classification comparison. The maximum utilization of the available data within multiple images and the optimized classification allowed for an improvement in the classification accuracy. The proposed rule-based image-classification method is expected to be widely employed for the time-series analysis of images to produce a thematic map for urban development and to monitor urban development and environmental change.

• Journal of the Korea Safety Management & Science
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• v.17 no.3
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• pp.143-148
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• 2015

The study examined the relationship between workers' safety awareness, safety performance and the components of the intelligent image analysis system in accordance with preventing the workers from safety hazard in dangerous working area. Based on the safety performance model, we include safety knowledge, safety motivation, safety compliance and safety participation, and we also define three additional factors of the intelligent image analysis system such as functional feature, penalty and incentive by using factor analysis. SEM(Structural Equation Modeling) analyses on the data from the total of 73 workers showed that functional feature of intelligent analysis system and incentive were positively related to safety knowledge and safety motivation. And mediation effects of the relationship were verified to safety compliance and safety participation through safety knowledge as well.

• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.438-447
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• 2024

The subjects of this study were college students who participate in Wushu in China. A total of 300 people, 189 male and 111 female, were sampled as research subjects through convenience sampling by sharing the questionnaire link online and sending emails. The research tool used in this study was a questionnaire. Based on the questionnaire that had secured reliability and validity in previous research, it was modified and supplemented to suit this study. The statistical analysis used for data analysis was frequency analysis, exploratory factor analysis, reliability analysis, and multiple regression analysis using SPSS Windows 20.0 Version. First, Wushu image was found to have a partial effect on self-congruity. Second, Wushu image was found to have a partial effect on loyalty. Third, the self-congruity of Wushu participants was found to partially affect loyalty.