• Korean Journal of Veterinary Research
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• v.29 no.1
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• pp.1-6
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• 1989

The number and distribution of the retinal ganglion cells in the 2 years old Korean native cattle was determined from whole fiat mounted preparation stained with methylene blue and thionin. The results were summarized as follows. 1. The total number of retinal ganglion cells was estimated to be 3,085,200 in the bovine retina ranging from $2,214mm^2$ in total area. 2. Visual streak was recognized at the area 2.5mm superior to the optic disc and ganglion cell density drops off rapidly to the directions superior to and inferior to the visual streak. 3. Area centralis ($6,800cells/mm^2$) was located at the area 10mm temporally from the point of 3mm superior to the optic disc. 4. The number of ${\alpha}-type$ ganglion cells (above $15{\mu}$) was 57,000 in the bovine retina and ${\alpha}-type$ ganglion cells constituted 18.5% of the total cells. 5. The relative frequency of ${\alpha}-type$ ganglion cells was higher in the peripheral regions than in the visual streak, especially higher in the superior-temporal quadrant than in other region of the bovine retina.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.113-117
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• 2020

Images were taken under various weather such as rain, haze, snow often show low visibility, which can dramatically decrease accuracy of some tasks in computer vision: object detection, segmentation. Besides, previous work to enhance image usually downsample the image to receive consistency features but have not yet good upsample algorithm to recover original size. So, in this research, we jointly implement removal streak in heavy rain image and super resolution using a deep network. We put forth a 2-stage network: a multi-model network followed by a refinement network. The first stage using rain formula in the single image and two operation layers (addition, multiplication) removes rain streak and noise to get clean image in low resolution. The second stage uses refinement network to recover damaged background information as well as upsample, and receive high resolution image. Our method improves visual quality image, gains accuracy in human action recognition task in datasets. Extensive experiments show that our network outperforms the state of the art (SoTA) methods.

• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.153-163
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• 2015

This paper proposes a rain detection and removal algorithm that is robust against camera motion in video sequences. In detection part, the proposed algorithm initially detects possible rain streaks by using intensity properties and spatial properties. Then, the rain streak candidates are selected based on Gaussian distribution model. In removal part, a non-rain block matching algorithm is performed between adjacent frames to find similar blocks to the block that has rain pixels. If the similar blocks to the block are obtained, the rain region of the block is reconstructed by non-local means (NLM) filter using the similar neighbors. Experimental results show that the proposed algorithm outperforms the previous works in terms of subjective visual quality of de-rained video sequences.

• Journal of the Korean Society of Radiology
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• v.12 no.1
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• pp.85-91
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• 2018

Computed tomography, which obtains section images from reconstruction process using projection images, has been applied to various fields. The spatial resolution of the reconstructed image depends on the device used in CT system, the object, and the reconstruction process. In this paper, we investigates the effect of the number of projection images and the pixel size of the detector on the spatial resolution of the reconstructed image under the parallel beam geometry. The reconstruction program was written in Visual C++, and the matrix size of the reconstructed image was $512{\times}512$. The numerical bar phantom was constructed and the Min-Max method was introduced to evaluate the spatial resolution on the reconstructed image. When the number of projections used in reconstruction process was small, artifact like streak appeared and Min-Max was also low. The Min-Max showed upper saturation when the number of projections is increased. If the pixel size of the detector is reduced to 50% of the pixel size of the reconstructed image, the reconstructed image was perfectly recovered as the original phantom and the Min-Max decreased as increasing the detector pixel size. This study will be useful in determining the detector and the accuracy of rotation stage needed to achieve the spatial resolution required in the CT system.

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.385-392
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• 2005

We developed an algorithm to efficiently detect streaks in survey images and made a performance test with YSTAR-NEOPAT images obtained by the 0.5m telescope stationed in South Africa. Fast moving objects whose apparent speeds exceed 10 arcsec/min are the main target of our algorithm; these include artificial satellites, space debris, and very fast Near-Earth Objects. Our algorithm, based on the outline shape of elongated sources employs a step of image subtraction in order to reduce the confusion caused by dense distribution of faint stars. It takes less than a second to find and characterize streaks present in normal astronomical images of 2K format. Comparison with visual inspection proves the efficiency and completeness of our automated detection algorithm. When applied to about 7,000 time-series images from YSTAR telescope, nearly 700 incidents of streaks are detected. Fast moving objects are identified by the presence of matching streaks in adjoining frames. Nearly all of confirmed fast moving objects turn out to be artificial satellites or space debris. Majority of streaks are however meteors and cosmic ray hits, whose identity is often difficult to classify.

• Korean journal of applied entomology
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• v.62 no.4
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• pp.299-305
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• 2023

Bemisia tabaci is one of polyphagous insect pests that transmits Tomato Yellow Leaf Curl Virus (TYLCV) and Cassava Brown Streak Disease (CBSD). Insecticides are primarily applied to control B. tabaci, but it has limits due to the development of resistance. As a result, a fixed precision sampling plan was developed for its integrated pest management (IPM). The tomato plants were divided into top (more than 130cm from the ground), middle (70 cm to 100 cm above the ground), and bottom (50 cm or less above the ground) strata, before visual sampling of the larvae of B. tabaci. The spatial distribution analysis was conducted using Taylor's power law coefficients with pooled data of top, middle, bottom strata. Fixed precision sampling plan and control decision-making were developed with precision levels and action threshold recommended from published scientific papers. To assess the validation of the developed sampling plans, independent data not used in the analysis were evaluated using the Resampling Validation for Sampling Plan (RVSP) program.