• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.83-88
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• 2005

In this paper, detection based - adaptive windowed nonlinear filters(DB-AWNF) are proposed for removing one-side impulse noise in infrared image. They are composed of impulse detector and window-size-variable median filters. Impulse detector checks whether current pixel is impulse or not using range function and nonlinear location estimator. If impulse is detected, current pixel is filtered according to four kinds of local masks by use of median filter. If not. current pixel is delivered to output like identity filter. In qualitative view, the proposed could have removed heavy corrupted noise up to $20\%$ and reserved the details of image. In quantitative view, PSNR was measured. The proposed could have 13 - 31[dB] more improved performance than those of median($3{\times}3$) filter and 18 - 25[dB] more improved performance than those of median($5{\times}5$) filter.

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.12-20
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• 2024

Side impact with MDB (moving deformable barrier) is common in side crash test protocols around the globe, most of which are quite similar to that of US NCAP side impact protocol but IIHS side impact protocol is considered to be the most harsh one due to the MDB's weight and impact speed. In this study US NCAP side impact and IIHS side impact test conditions are compared with respect to delta-V (impulse of the test vehicle), roll speed, and yaw speed as well as survival space (the smallest distance between the front driver seat cushion center to B pillar after the test). Error factors (friction between tire and ground, tolerance of vertical and longitudinal position of the MDB with respect to the test vehicle), which are resident in the test protocol is studied with respect to the global vehicle behavior (delta-V, roll, yaw) as well as survival space.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.41-45
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• 2010

Image processing techniques are fundamental in human vision-based image information processing. There have been widely studied areas such as image transformation, image enhancement, image restoration, and image compression. One of research subgoals in those areas is enhancing image information for the correct information retrieval. As a fundamental task for the image recognition and interpretation, image enhancement includes noise filtering techniques. Conventional filtering algorithms may have high noise removal rate but usually have difficulty in conserving boundary information. As a result, they often use additional image processing algorithms in compensation for the tradeoff of more CPU time and higher possibility of information loss. In this paper, we propose a Fuzzy Mask Filtering algorithm that has high noise removal rate but lesser problems in above-mentioned side-effects. Our algorithm firstly decides a threshold based on fuzzy logic with information from masks. Then it decides the output pixel value by that threshold. In a designed experiment that has random impulse noise and salt pepper noise, the proposed algorithm was more effective in noise removal without information loss.