• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1409-1416
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• 2012

An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• Journal of the Korean Institute of Rural Architecture
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• v.15 no.2
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• pp.1-8
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• 2013

Traditional markets located in local small towns have effected local economies and culture for a long time, but their current situation is not so good. In this context, this paper tries to find out the physical characteristics and problems of local traditional markets and to extract some suggestions to improve them. For this, I selected three traditional markets as case study areas such as Mugeuk, Samseong, and Boeun located in local small town in Chungbuk Province. Using the diverse survey methods including theoretical review, basic data analysis, field survey, and questionnaire study, I examined physical features such as location, spatial structure, type of business, streetscape, and convenient facilities in the designated markets. Finally, based on this, I proposed some suggestions on the improvement of traditional markets located in local small towns as followings; expand and improve the convenient facilities, enforce the image of entrance, boost and form groups of specified business, reflect the local characteristics, and amend the related institution etc.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.3
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• pp.67-74
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• 2014

Over the last several decades, the United States has experienced a great number of natural disasters. To minimize the impact of the natural hazard events, the U.S. government spent a tremendous amount of money through federal assistant programs. To be eligible for the programs, a mitigation project must be cost effective (more benefits compared to project costs). However, the state and local communities suffering from the natural disasters generally have difficulty in collecting reliable evidence for their damages which can be converted later into benefits when a mitigation project is implemented. Therefore, this paper shows the process of conducting a benefit cost analysis with limited data. Besides, it also provides how to apply the limited data to the analysis through a case study. Consequently, this paper help state and local communities get funding from the federal government, which in turns will improve the image of construction industry by preventing people from natural disasters.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1329-1331
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• 2003

Improvement of the classification accuracy is one of the major concerns in the field of remote sensing application research in recent years. Previous research shows that the accuracy of the conventional classification methods based only on the original spectral information were usually unsatisfied and need to be refined by manual edit. This present paper describes a method of combining the image processing, ancillary data (such as digital elevation model) and expert knowledge (especially the knowledge of local professionals) to improve the efficiency and accuracy of the satellite image classification in hilly land. Firstly, the Landsat TM data were geo-referenced. Secondly, the individual bands of the image were intensitynormalized and the normalized difference vegetation index (NDVI) image was also generated. Thirdly, a set of sample pixels (collected from field survey) were utilized to discover their corresponding DN (digital number) ranges in the NDVI image, and to explore the relationships between land use type and its corresponding spectral features . Then, using the knowledge discovered from previous steps as well as knowledge from local professionals, with the support of GIS technology and the ancillary data, a set of conditional statements were applied to perform the TM imagery classification. The results showed that the integration of image processing and spatial analysis functions in GIS improved the overall classification result if compared with the conventional methods.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.719-722
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• 2019

Gamma-ray images generally suffer from a lot of noise because of low photon detection in the gamma camera system. The purpose of this study is to improve the image quality in gamma-ray images using a gamma camera system with a fast nonlocal means (FNLM) noise reduction algorithm with an acceleration function. The designed FNLM algorithm is based on local region considerations, including the Euclidean distance in the gamma-ray image and use of the encoded information. To evaluate the noise characteristics, the normalized noise power spectrum (NNPS), contrast-to-noise ratio (CNR), and coefficient of variation (COV) were used. According to the NNPS result, the lowest values can be obtained using the FNLM noise reduction algorithm. In addition, when the conventional methods and the FNLM noise reduction algorithm were compared, the average CNR and COV using the proposed algorithm were approximately 2.23 and 7.95 times better than those of the noisy image, respectively. In particular, the image-processing time of the FNLM noise reduction algorithm can achieve the fastest time compared with conventional noise reduction methods. The results of the image qualities related to noise characteristics demonstrated the superiority of the proposed FNLM noise reduction algorithm in a gamma camera system.

• Journal of Distribution Science
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• v.22 no.7
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• pp.63-71
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• 2024

Purpose: The main objective of this research is to encourage the increase of local MSME businesses through the sustainable tourism sector in South Sulawesi by proposing a research model that focuses on increasing revisit intention. This, in turn, is expected to stimulate local trade and strengthen tourism attractiveness. Research design, data and methodology: A quantitative research method involving 190 domestic tourist respondents was employed, utilizing a questionnaire for data collection. Structural Equation Modeling (SEM) analysis through AMOS software was applied, and the Sobel test to assess indirect effects. Results: The research findings indicate that memorable customer experiences and travel motivations significantly influence destination image. However, travel motivation does not significantly affect revisit intention. Furthermore, memorable customer experiences and destination image significantly impact revisit intention. Notably, destination image plays a significant mediating role in the relationship between travel motivation and increased revisit intention. Conclusions: Memorable customer experiences and travel motivations directly contribute to the formation of a more positive destination image. Furthermore, memorable customer experiences drive the revisit intention, but travel motivation is not significant. Memorable customer experiences only influence revisit intention through the formed destination image. Additionally, the improvement of memorable customer experience and destination image increased revisit intention.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.61-67
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• 2017

The detection of oil spills using radar image has been studied extensively. However, most of the proposed techniques have been focused on improving detection accuracy through the advancement of algorithms. In this study, research has been conducted to improve the accuracy of oil spill detection by improving the quality of radar images, which are used as input data to detect oil spills. Thresholding algorithms were used to measure the image improvement both before and after processing. The overall accuracy increased by approximately 16%, the producer accuracy increased by 40%, and the user accuracy increased by 1.5%. The kappa coefficient also increased significantly, from 0.48 to 0.92.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.3
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• pp.322-326
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• 1988

The sigma filter is a nonlinear filter of modifying average filter to develop edge-preserving characteristics. However, this filter is yet weak to the impulsive noise such as BSC noise. Therefore it has not been used so highly in the image restoration area. In this paper, We propose an adaptive image restoration algorithm using the local statistic and the characteristic of human eyes in order to compensate its drawback and to improve its performance. The performance of the proposed algorithm and the vonventional ones are compared for images degraded by BSC noise. The proposed algorithm shows better performance than the median filter and yields 5 dB performance improvement over the convertional K-sigma filter on SNR gain.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.863-867
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• 1987

When digital image signals are transmitted or stored, they may be usually degraded by impulsive noise such as BSC noise. Though median filtering is a very effective method to reduce the impulsive noise, it brings non-negligible distortion after filtering. Several algorithms have been proposed to reduce such a distortion, but their reconstructed image quality are inadequate in some cases and they have a difficulty in real-time processing. In this paper, an effective filtering algorithm which can not only reduce the noise effectively but also preserve the edges well and lessen the distortion greatly, is presented. The proposed algorithm is an adaptive algorithm of median filter using local statistics, based on the characteristics of human eyes. The adaptive algorithm results shwo performance improvement of up to 3-4 dB over the nonadaptive one.

• Journal of Information Processing Systems
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• v.16 no.4
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• pp.991-1000
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• 2020

We propose a content-based image retrieval system that uses a combination of completed local binary pattern (CLBP) and color autocorrelogram. CLBP features are extracted on a multi-resolution multi-direction filtered domain of value component. Color autocorrelogram features are extracted in two dimensions of hue and saturation components. Experiment results revealed that the proposed method yields a lot of improvement when compared with the methods that use partial features employed in the proposed method. It is also superior to the conventional CLBP, the color autocorrelogram using R, G, and B components, and the multichannel decoded local binary pattern which is one of the latest methods.