• Fisheries and Aquatic Sciences
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• v.4 no.1
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• pp.10-17
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• 2001

One serious problem among the troubles to identify objects in an optical microscopic image is contour background due to non-uniform light source and various transparency of samples. To solve this problem, this paper proposed an elimination method of the contour background and compensation technique as follows. First, Otsu's optimal thresholding method extracts pixels representing background. Second, bilinear interpolation finds non-deterministic background pixels among the sampled pixels. Third, the 2D cubic fitting method composes surface function from pivoted background pixels. Fourth, reconstruction procedure makes a contour image from the surface function. Finally, elimination procedure subtracts the approximated background from the original image. To prove the effectiveness of the proposed algorithm, this algorithm is applied to the yeast Zygosaccharomyces rouxii and ammonia-oxidizing bacteria Acinetobacter sp. Labeling by this proposed method can remove some noise and is more exact than labeling by only Otsu's method. Futhermore, we show that it is more effective for the reduction of noise.

• Journal of Information Technology Applications and Management
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• v.30 no.1
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• pp.11-19
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• 2023

To remove unnecessary background from tooth X-ray images and enhance the sharpness of tooth and gum images, image processing techniques including contrast adjustment and histogram equalization are used. The introduction of two methods for detecting the boundary of the tooth and gum region and separating the tooth and gum from the background. In both cases, the background of the tooth X-ray images could be removed as a result, improving the quality of the images. The proposed method improves MTF (Modulation Transfer Function), an image performance indicator, as a result of measuring MTF. The original image's spatial frequency ranged from 4.73 to 11.40 lp/mm at the 10% response, whereas the proposed image's spatial frequency ranged from 10.90 to 11.85 lp/mm, giving uniformly enhanced results. In contrast, tooth and gums could not be completely separated from the background using Apple's Lift subject from background function.

• Korean Journal of Clinical Pharmacy
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• v.29 no.2
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• pp.101-108
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• 2019

Background: High doses of methotrexate (MTX) are often used in various chemotherapy protocols to treat acute lymphoblastic leukemia (ALL) and non-Hodgkin's lymphoma (NHL) in children, but its delayed elimination increases the occurrence of adverse events, such as bone marrow suppression. The aim of this study was to investigate the elimination of MTX at 24 and 48 hours. Methods: We retrospectively analyzed electronic medical records of ALL or NHL pediatric patients who received $5g/m^2$ MTX infusion over 24 hours (between June, 2012 and July, 2018) at the Yonsei University Health System, Korea. The delayed elimination of MTX concentrations was assessed with 100 or $150{\mu}M$ MTX at 24 hours, and 2 or $5{\mu}M$ at 48 hours. Results: Among the 85 MTX cycles administered, 23 cycles were classified in delayed elimination group, and 62 cycles showed normal elimination. At 24 hours, the delayed elimination group with MTX concentration > $100{\mu}M$ showed higher percentage than group with MTX concentration < $100{\mu}M$ (45.8% vs. 19.7%, p = 0.015). However, no differences were observed at $150{\mu}M$ MTX (p = 0.66). At 48 hours, the delayed elimination was higher than the normal elimination at both concentration baselines (p < 0.001 at $2{\mu}M$, p = 0.024 at $5{\mu}M$). Conclusions: MTX concentrations greater than $100{\mu}M$ show high probability of delayed elimination at 24 hours. When MTX levels are above normal, leucovorin and hydration regimens should be continued to prevent delayed elimination.

• Journal of Korea Multimedia Society
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• v.11 no.5
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• pp.610-622
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• 2008

Moving object segmentation is an essential technique for various video surveillance applications. The result of moving object segmentation often contains shadow regions caused by the color difference of shadow pixels. Hence, moving object segmentation is usually followed by a shadow elimination process to remove the false detection results. The common assumption adopted in previous works is that, under the illumination variation, the value of chromaticity components are preserved while the value of intensity component is changed. Hence, color transforms which separates luminance component and chromaticity component are usually utilized to remove shadow pixels. In this paper, various color spaces (YCbCr, HSI, normalized rgb, Yxy, Lab, c1c2c3) are examined to find the most appropriate color space for shadow elimination. So far, there have been some research efforts to compare the influence of various color spaces for shadow elimination. However, previous efforts are somewhat insufficient to compare the color distortions under illumination change in diverse color spaces, since they used a specific shadow elimination scheme or different thresholds for different color spaces. In this paper, to relieve the limitations of previous works, (1) the amount of gradients in shadow boundaries drawn to uniform colored regions are examined only for chromaticity components to compare the color distortion under illumination change and (2) the accuracy of background subtraction are analyzed via RoC curves to compare different color spaces without the problem of threshold level selection. Through experiments on real video sequences, YCbCr and normalized rgb color spaces showed good results for shadow elimination among various color spaces used for the experiments.

• Fashion & Textile Research Journal
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• v.15 no.6
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• pp.874-885
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• 2013

This study analyzes the visage in fashion illustration based on the deconstructed visage of Francis Bacon's paintings as well as analyzes fashion illustration works since 2000. The deconstructed visages in Francis Bacon's paintings are classified as blurring, elimination, distortion and division. The expressive methods and meanings in fashion illustration (according to categorization) are as follow. Blurring shows an ambiguous visage organ by the sweeping of the brush, removal of a boundary among the visage, body and clothes, gradation of organic line like visage shapes, stretching of the a plat combined to visage and fragmentation of visage. It represents an uncertainty of the fashion theme and image interpretation, impossibility of figure by ambiguity, fantastic effect and the induction of the uncanny. Elimination shows the background color's painting of a photo-montage, overlap of a cutting of visage's part and background of a plat, elimination of the visage and the elimination of eyes, nose or lips. It represents a weakened identity, the reinforcement of anonymity, creation of a violent image, and uncanny unfamiliarity. Distortion shows a distorted visage by free drawing, and unconscious drawing line, fluid digital body, combination of an unconscious curve, and an eccentric combination of the accidental. It represents the relief of specialty about realistic existence, hypothetical immateriality and fantasy. Division shows overlapped visages with different angles, the weird combination of a plural visage and different species and a plural breakaway of direction, and the position of several organs. It represents motion by power's trace, non-territory of species, ambiguity and uncertainty and the uncanny.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.1-8
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• 2008

Generally, moving objects in surveillance video are extracted by background subtraction or frame difference method. However, moving cast shadows on object distort extracted figures which cause serious detection problems. Especially, analyzing vehicle information in video frames from a fixed surveillance camera on road, we obtain inaccurate results by shadow which vehicle causes. So, Shadow Elimination is essential to extract right objects from frames in surveillance video. And we use shadow removal algorithm for vehicle classification. In our paper, as we suppress moving cast shadow in object, we efficiently discriminate vehicle types. After we fit new object of shadow-removed object as three dimension object, we use extracted attributes for supervised learning to classify vehicle types. In experiment, we use 3 learning methods {IBL, C4.5, NN(Neural Network)} so that we evaluate the result of vehicle classification by shadow elimination.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.11-19
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• 2008

Background subtraction, which separates moving objects in video sequences, is an essential technology for object recognition and tracking. However, background subtraction methods are often confused by shadow regions and this misclassification of shadow regions disturbs further processes to perceive the shapes or exact positions of moving objects. This paper proposes a method for shadow elimination which is based on shadow modeling by color information and Bayesian classification framework. Also, because of dynamic update of modeling parametres, the proposed method is able to correspond adaptively to illumination changes. Experimental results proved that the proposed method can eliminate shadow regions effectively even for circumstances with varying lighting condition.

• Journal of Ginseng Research
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• v.44 no.6
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• pp.784-789
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• 2020

Background: The separation of isomeric compounds from a mixture is a recurring problem in chemistry and phytochemistry research. The purification of pharmacologically active ginsenoside Rb₃ from ginseng extracts is limited by the co-existence of its isomer Rb₂. The aim of the present study was to develop an enzymatic elimination-combined purification method to obtain pure Rb₃ from a mixture of isomers. Methods: To isolate Rb₃ from the isomeric mixture, a simple enzymatic selective elimination method was used. A ginsenoside-transforming glycoside hydrolase (Bgp2) was employed to selectively hydrolyze Rb₂ into ginsenoside Rd. Ginsenoside Rb₃ was then efficiently separated from the mixture using a traditional chromatographic method. Results: Chromatographic purification of Rb₃ was achieved using this novel enzymatic elimination-combined method, with 58.6-times higher yield and 13.1% less time than those of the traditional chromatographic method, with a lower minimum column length for purification. The novelty of this study was the use of a recombinant glycosidase for the selective elimination of the isomer. The isolated ginsenoside Rb₃ can be used in further pharmaceutical studies. Conclusions: Herein, we demonstrated a novel enzymatic elimination-combined purification method for the chromatographic purification of ginsenoside Rb₃. This method can also be applied to purify other isomeric glycoconjugates in mixtures.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.17 no.1
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• pp.47-54
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• 2013

This work deals with automatic motion detection for with surveillance tracking that aims to provide high-lighting movable objects which is discriminated from moving backgrounds such as moving trees, etc. For this aim, we perform a false background region detection together with an initial foreground detection. The false background detection detects the moving backgrounds, which become eliminated from the initial foreground detection. This false background detection is done by performing the bimodal segmentation on a deformed image, which is constructed using the information of the dominant colors in the background.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.171-182
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• 2007

We propose a simple method for tracking a nonparameterized subject contour in a single video stream with a moving camera and changing background. Then we present a method to eliminate the tracked contour object by replacing with the background scene we get from other frame. First we track the object using LOD (Level-of-Detail) canny edge maps, then we generate background of each image frame and replace the tracked object in a scene by a background image from other frame that is not occluded by the tracked object. Our tracking method is based on level-of-detail (LOD) modified Canny edge maps and graph-based routing operations on the LOD maps. We get more edge pixels along LOD hierarchy. Our accurate tracking is based on reducing effects from irrelevant edges by selecting the stronger edge pixels, thereby relying on the current frame edge pixel as much as possible. The first frame background scene is determined by camera motion, camera movement between two image frames, and other background scenes are computed from the previous background scenes. The computed background scenes are used to eliminate the tracked object from the scene. In order to remove the tracked object, we generate approximated background for the first frame. Background images for subsequent frames are based on the first frame background or previous frame images. This approach is based on computing camera motion. Our experimental results show that our method works nice for moderate camera movement with small object shape changes.