• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.58-61
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• 2000

In this paper, we propose a new performance comparison method of various Interpolation methods for image enlargement The conventional methods employs PSNR and edge characteristic evaluation for performance comparison of interpolation methods. The proposed performance comparison method uses the position Information for each difference pixel's value and the frequency characteristic information between original image and Interpolated image. The proposed methods might be useful for performance comparison of various Interpolation methods through the computer simulation.

• Journal of the Korean association of regional geographers
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• v.5 no.1
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• pp.137-150
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• 1999

Five land use change detection methods were applied to 1994 and 1997 Landsat Thematic Mapper (TM) images of Pook-Gu, Taegu city to determine the land-cover changes between the two dates. The two images were coregistred to UTM coordinates. A post-classification comparison method was the most commonly used quantitative method of change detection. A pre-classification comparison method was more effective method to change detection of land cover than a post-classification comparison method. Two indices were used to assess the accuracies of the studied methods. A image differencing method was found to be most accurate for detecting change verse no change among five land use change detection methods. The difference image of band 2 was found to be most accurate. The overall accuracy and Kappa index agreement of the difference image of band 2 were 0.810 and 0.447.

• 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

• Fashion & Textile Research Journal
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• v.22 no.4
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• pp.469-480
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• 2020

The study investigated the effects of SNS usage on 20s and 30s female SNS users' internalization of thin body, body surveillance, appearance upper comparison, body dissatisfaction and eating disorders based on objectification theory and social comparison theory. The study examined differences on SNS usage and body image variables between high and low groups of SNS envy and self-compassion. Social Network Service (SNS) is used as a contemporary marketing tool for brands and companies. It also influences the body image of female SNS users. The survey used an online survey company for female SNS users in their 20s and 30s to analyze the effect of SNS usage. The results showed that SNS usage significantly impacted the internalization of a thin body, body surveillance and appearance upper comparison. The internalization of thin body also had a significant impact on body surveillance and appearance upper comparison. Appearance upper comparison positively affected body dissatisfaction and eating disorders. Finally, the group with higher SNS envy showed higher SNS usage, internalization of thin body, body surveillance, appearance upper comparison, body dissatisfaction and eating disorders. The group with higher self-compassion showed opposite results. This study provided a theoretical expansion for a SNS and female body image study with objectification theory and social comparison theory. It also suggests positive SNS marketing strategies use for brands. Lastly, this study emphasized the importance of the proper use of SNS to protect the body image of SNS users.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.67-75
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• 2023

Recently, many studies have been conducted on ways to utilize AI technology in NIDS (Network Intrusion Detection System). In particular, CNN-based NIDS generally shows excellent performance. CNN is basically a method of using correlation between pixels existing in an image. Therefore, the method of generating an image is very important in CNN. In this paper, the performance comparison of CNN-based NIDS according to the image generation method was performed. The image generation methods used in the experiment are a direct conversion method and a one-hot encoding based method. As a result of the experiment, the performance of NIDS was different depending on the image generation method. In particular, it was confirmed that the method combining the direct conversion method and the one-hot encoding based method proposed in this paper showed the best performance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.517-518
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• 2007

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.563-564
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• 2023

Midjourney and Stable Diffusion are two popular AI-generated image programs nowadays. With AI's outstanding image-generation capabilities, everyone can create artistic paintings in just a few minutes. Therefore, "Comparison of differences between AI-generated images using Midjourney and Stable Diffusion" will help see each program's advantages and assist the users in identifying the tool suitable for their needs.

• Journal of Intelligence and Information Systems
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• v.11 no.2
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• pp.1-15
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• 2005

The explosive growth of the Internet leads to various on-line shopping malls and active E-Commerce. however, as the internet has experienced continuous growth, users have to face a variety and a huge amount of items, and often waste a lot of time on purchasing items that are relevant to their interests. To overcome this problem the comparison shopping systems, which can help to compare items' information with those other shopping malls, have been issued as a solution. However, when users do not have much knowledge what they want to find, a keyword-based searching in the existing comparison shopping systems lead users to waste time for searching information. Thereby, the performance is fell down. To solve this problem in this research, we suggest the Comparison Shopping System using Image Retrieval based on Semantic Web. The proposed system can assist users who don't know items' information that they want to find and serve users for quickly comparing information among the items. In the proposed system we use semantic web technology. We insert the Semantic Annotation based on Ontology into items' image of each shopping mall. Consequently, we employ those images for searching the items instead of using a complex keyword. In order to evaluate performance of the proposed system we compare our experimental results with those of Keyword-based Comparison Shopping System and simple Semantic Web-based Comparison Shopping System. Our result shows that the proposed system has improved performance in comparison with the other systems.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.367-373
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• 2000

Natural and artificial land features are very dynamic, changing somewhat repidly in our lifetime. It is important that such changes are inventoried accurately so that the physical and human processes at work can be more fully understood. Change detection is a technique used to determine the change between two or more time periods of a particular object of study. Change detection is an important process in monitoring and managing natural resources and urban development because it provides quantitative analysis of the spatial distribution in the population of interest. The purpose of this research is to detect environmental changes surrounding an area of Mountain Moscow, Idaho using Landsat Thematic Maper (TM) images of (July 8, 1990 and July 20, 1991). For accurate classification, the Image enhancement process was performed for improving the image quality of each image. A SPOT image (Aug. 14, 1992) was used for image merging in this research. Supervised classification was performed using the maximum likelihood method. Accuracy assessments were done for each classification. Two images were compared on a pixel-by-pixel basis using the post-classification comparison method that is used for detecting the changes of the study area in this research. The 'from-to' change class information can be detected by post classification comparison using this method and we could find which class change to another.

• Journal of Korea Multimedia Society
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• v.14 no.12
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• pp.1544-1548
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• 2011

A process of choosing a subset of original features, so called feature selection, is considered as a crucial preprocessing step to image processing applications. There are already large pools of techniques developed for machine learning and data mining fields. In this paper, basically two methods, non-feature selection and feature selection, are investigated to compare their predictive effectiveness of classification. Color co-occurrence feature is used for defining image features. Standard Sequential Forward Selection algorithm are used for feature selection to identify relevant features and redundancy among relevant features. Four color spaces, RGB, YCbCr, HSV, and Gaussian space are considered for computing color co-occurrence features. Gray-level image feature is also considered for the performance comparison reasons. The experimental results are presented.