• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.505-513
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• 2004

Most video codecs including the international standards use the block-based hybrid structure for efficient compression. But for low bit-rate applications such as video transmission through wireless channels, the blocking artifacts degrade image qualify seriously. In this paper, we propose an adaptive de-blocking algorithm using characteristics of the block boundaries. Blocking artifacts contain the high frequency components near the block boundaries, therefore the lowpass filtering can remove them. However, simple lowpass filtering results into blurring by removing important information such as edges. To overcome this problem, we determine the modes depending upon the characteristics of pixels adjacent to block boundary then proper filter is applied to each area. Simulation results show that proposed method improves de-blocking performance compared to that of MPEG-4.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.689-698
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• 2006

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.171-179
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• 2020

In this study, a local corrosion environment monitoring was conducted using steel box specimen fabricated to be the same as actual steel bridge members. The steel box specimen that obtained the same corrosion environment as a steel bridge was classified into the upper plate, bottom plate and web plate. Atmospheric corrosion monitoring sensors(ACM sensors) were installed in each corrosion monitoring member of a steel box specimen to measure the corrosion current and examine time of wetness for each monitoring member. The time of wetness and accumulated corrosion current of each monitoring member were calculated from the measured corrosion current using ACM sensors. The corrosion environment that appeared for each of the steel box members was evaluated from monitoring corrosion environment data as the corrosion current, time of wetness, mean corrosion depth of each monitoring member. Additionally, the atmospheric corrosion environment monitoring was also conducted to compare with the local corrosion environment of steel box members. From these local corrosion environment monitoring for the steel box specimen, the relationship between the relative corrosion environment and mean corrosion depth of each steel box member was examined.