• Journal of Animal Environmental Science
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• v.11 no.1
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• pp.35-44
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• 2005

The objectives of this study were to develop an image processing algorithm for finding the sick or the dead layers(SDL) rearing in the multi-tier layer battery, which is a core technology of remote monitoring systems for layers, and to test the performance of algorithm developed in the experimental poultry housing. Based on the literature study and experiment, the standing up of layer was set as a criterion for judging layers whether sick or dead. Then, by the criterion set, an algorithm was developed. The image processing algorithm developed was tested how well it could and SDL at the experimental poultry housing. Test results showed that its monitoring correctness of layers standing up in the cages having all healthy layers was $92\%$, and $96\%$ in the cages having SDL. Therefore, it would be concluded that the image processing algorithm developed in this study was well suited to the purpose of development.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.100-100
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• 2017

하천에 유입된 오염물질의 2차원 혼합거동은 하천 주흐름에 의한 이송현상과 유속 성분의 수심평균 값에 대한 공간적 편차로부터 야기되는 분산현상으로 설명 할 수 있다. 이는 3차원 이송확산 방정식으로부터 수심 적분된 2차원 이송-분산 방정식으로 수학적 유도가 가능하며, 수심방향으로 적분하는 과정에서 발생되는 농도의 분산항은 Taylor Dispersion 개념에 기초하여 종방향 및 횡방향의 2차원 분산계수로 표현된다. Fischer(1978)는 연직방향 유속분포로부터 2차원 분산계수를 추정하는 해석해를 수학적으로 유도하였으나, 실제 하천에서 정밀한 연직방향 유속분포를 계측하는 것은 많은 비용 및 노동력을 초래한다. 따라서 선행 연구자들은 2차원 혼합모형의 분산계수를 산정하고자 실험적 방법으로써 추적자실험을 수행하였다. 추적자실험은 추적자 물질을 수체에 주입한 후 농도의 변화를 관측함으로써 추적자물질이 하천에서 이송 및 분산되는 과정을 이해하는데 유용하다. 기존의 추적자실험은 고정된 위치에서 농도를 계측하여 시계열적인 농도의 변화를 관측한 후, 오염운 동결가정을 통해 종,횡방향 분산계수의 산정이 가능하지만, 오염물질 농도의 공간적 분포를 얻기에는 한계가 있다. 본 연구에서는 기존의 추적자실험법의 한계를 극복하고자 형광물질을 이용한 추적자실험을 수행함과 동시에 드론에 장착된 디지털카메라를 이용하여 항공영상을 취득 및 분석하여, 하천에 주입된 형광물질의 농도분포를 시공간적으로 추출하는 기법을 개발하고, 이를 바탕으로 오염물질의 2차원 혼합거동을 분석하였다. 본 실험은 한국건설기술연구원의 안동하천실험센터의 A3실험수로에서 수행되었으며, 실험수로는 평균 하폭 5 m, 평균 수심 0.44 m, 유량 $0.96m^3/s$의 실제 소규모 하천과 유사한 축척을 가지고 있다. 추적자물질은 Rhodamine WT 용액이 사용되었으며, 실험수로 내 설치된 15개의 형광광도계(YSI-600OMS)를 이용하여 농도를 측정하였다. 항공영상의 취득을 위해 이용된 드론은 DJI-Phantom 3 Professional 이며, 3840x2160의 해상도로 초당 30 frame의 동영상으로 취득되었다. 영상의 정합 및 좌표화를 위해 RTK-GPS를 이용하여 12개의 지상 기준점의 좌표를 취득한 후, 사영변환을 통해 영상좌표를 지상좌표로 변환하였다. 영상의 픽셀값을 농도장으로 변환하기 위해 각 RGB 밴드의 픽셀값을 통계적으로 분석하여 농도장으로 변환하였으며, 영상으로부터 얻은 농도장은 형광광도계에 의해 실측된 농도와 결정계수 0.9이상의 수준으로 정확도를 나타냈다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.621-628
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• 2021

This paper proposes a method to calibrate a thermal camera with three different perspectives. In particular, the intrinsic parameters of the camera and re-projection errors were provided to quantify the accuracy of the calibration result. Three lenses of the camera capture the same image, but they are not overlapped, and the image resolution is worse than the one captured by the RGB camera. In computer vision, camera calibration is one of the most important and fundamental tasks to calculate the distance between camera (s) and a target object or the three-dimensional (3D) coordinates of a point in a 3D object. Once calibration is complete, the intrinsic and the extrinsic parameters of the camera(s) are provided. The intrinsic parameters are composed of the focal length, skewness factor, and principal points, and the extrinsic parameters are composed of the relative rotation and translation of the camera(s). This study estimated the intrinsic parameters of thermal cameras that have three lenses of different perspectives. In particular, image enhancement based on a deep learning algorithm was carried out to improve the quality of the calibration results. Experimental results are provided to substantiate the proposed method.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1353-1369
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• 2023

In the event of a disaster accident at sea, the scale of damage will vary due to weather effects such as wind, currents, and tidal waves, and it is obligatory to minimize the scale of damage by establishing appropriate control plans through quick on-site identification. In particular, it is difficult to identify pollutants that exist in a thin film at sea surface due to their relatively low viscosity and surface tension among pollutants discharged into the sea. Therefore, this study aims to develop an algorithm to detect suspended pollutants on the sea surface in RGB images using imaging equipment that can be easily used in the field, and to evaluate the performance of the algorithm using input data obtained from actual waters. The developed algorithm uses image enhancement techniques to improve the contrast between the intensity values of pollutants and general sea surfaces, and through histogram analysis, the background threshold is found,suspended solids other than pollutants are removed, and finally pollutants are classified. In this study, a real sea test using substitute materials was performed to evaluate the performance of the developed algorithm, and most of the suspended marine pollutants were detected, but the false detection area occurred in places with strong waves. However, the detection results are about three times better than the detection method using a single threshold in the existing algorithm. Through the results of this R&D, it is expected to be useful for on-site control response activities by detecting suspended marine pollutants that were difficult to identify with the naked eye at existing sites.

• Journal of The Korean Society of Grassland and Forage Science
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• v.36 no.4
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• pp.365-369
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• 2016

Red sorrel (Rumex acetosella L.), as one of exotic weeds in Korea, was dominated in grassland and reduced the quality of forage. Improving current pasture productivity by precision management requires practical tools to collect site-specific pasture weed data. Recent development in unmanned aerial vehicle (UAV) technology has offered cost effective and real time applications for site-specific data collection. To map red sorrel on a hill pasture, we tested the potential use of an UAV system with digital cameras (visible and near-infrared (NIR) camera). Field measurements were conducted on grazing hill pasture at Hanwoo Improvement Office, Seosan City, Chungcheongnam-do Province, Korea on May 17, 2014. Plant samples were obtained at 20 sites. An UAV system was used to obtain aerial photos from a height of approximately 50 m (approximately 30 cm spatial resolution). Normalized digital number values of Red, Green, Blue, and NIR channels were extracted from aerial photos. Multiple linear regression analysis results showed that the correlation coefficient between Rumex content and 4 bands of UAV image was 0.96 with root mean square error of 9.3. Therefore, UAV monitoring system can be a quick and cost effective tool to obtain spatial distribution of red sorrel data for precision management of hilly grazing pasture.

• Korean Journal of Food Science and Technology
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• v.28 no.4
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• pp.786-789
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• 1996

An image processing system was built to evaluate the color properties of apple and meat. The system consisted of video camera, video card, 32 bit microcomputer and an optical illuminator. The operating software was developed to carry out capturing, analyzing, displaying and storing of the 8 bit digitized images of food. The images of apples at various maturing stages were investigated to obtain the color histogram of R, G, B and Hunter value. RGB histogram showed a major difference in G value, 35.01, the minor change in R value, 6.16, and the negligible difference in B value. The image of beef cut was separated into two parts, fat and lean tissue, by applying threshold value method based on the digital value of color. The threshold value for fat was over 240 and for lean under 230 in R value, respectively. The resulting non fat image showed 2% decreased color difference value, ${\Delta}E$, than whole meat cut.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.4
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• pp.143-154
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• 2004

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multi-spectral automatic Aerial photographic system(PKNU 2). This system's Multi-spectral camera can catch the visible(RGB) and infrared(NIR) bands($3032{\times}2008$ pixels) image. Visible and infrared bands images were obtained from each camera respectively and produced Color-infrared composite images to be analyzed in the purpose of the environment monitor but that was not very good data. Moreover, it has a demerit that the stereoscopic overlap area is not satisfied with 60% due to the 12s storage time of each data, while it was possible that PKNU 2 system photographed photos of great capacity. Therefore, we have been developing the advanced PKNU 2(PKNU 3) that consists of color-infrared spectral camera can photograph the visible and near infrared bands data using one sensor at once, thermal infrared camera, two of 40 G computers to store images, and MPEG board to compress and transfer data to the computer at the real time and can attach and detach itself to a helicopter. Verification and calibration of each sensor(REDLAKE MS 4000, Raytheon IRPro) were conducted before we took the aerial photographs for obtaining more valuable data. Corrections for the spectral characteristics and radial lens distortions of sensor were carried out.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1122-1129
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• 2015

This paper suggests a method of detecting traffic lights for vehicles by combining the HSV(hue saturation value) color model, morphometric characteristics, and location information appearing on consecutive images in daytime. In order to detect the traffic light, the color corresponding to the signal lights should be explored. It is difficult to detect traffic lights among colors of lights from buildings, taillight of cars, leaves, placards, etc. The proposed algorithm searches for the traffic lights from many candidates using morphometric characteristics and location information in consecutive images. The recognition process is divided into three steps. The first step is to detect candidates after converting RGB channel into HSV color model. The second step is to extract the boundaries between the housing of traffic lights and background by exploiting the assumption that the housing has lower brightness than the surrounding background. The last step is to recognize the signal light after eliminating the false candidates using morphometric characteristics and location information appearing on consecutive images. This paper demonstrates successful detection results of traffic lights from various images captured on the city roads.

• Journal of Information Technology Applications and Management
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• v.12 no.1
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• pp.231-239
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• 2005

This paper describes a new algorithm for pixel-based skin color detection to differentiate human form in color images by the ratio of R to H. In order to detect skin color efficiently, we examine the distribution of the R, G and B color elements combining to constitute the skin color in various color images. It shows that R is located in a narrower area than G and B on the RGB color space. And skin color is more related to R than G and B. Meanwhile, when the color image is transformed to the HSI color space, the S is variously changed in accordance with skin colors. The I is changed in accordance with the quantity and angle of light. But the H is less influenced by other conditions except for color. On the basis of the aforementioned study, we propose that the threshold for skin color detection is decided by the ratio of R to H. The proposed method narrows down the range of threshold, detects more skin color and reduces mis-detection of skin color in comparison to detection by R or H. In experimentation. it shows that the proposed algorithm overcomes changes of brightness and color to detect skin color in color images.

• Atmosphere
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• v.18 no.1
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• pp.1-14
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• 2008

The cloud amount, one of the basic parameter in atmospheric observation, have been observed by naked eyes of observers, which is affected by the subjective view. In order to ensure reliable and objective observation, a new algorithm to retrieve cloud amount was constructed using true color images composed of red, green and blue (RGB). The true color image is obtained by the Skyview, an all-sky imager taking pictures of sky, at the Science Building of Yonsei University, Seoul for a year in 2006. The principle of distinguishing clear sky from cloudy sky lies in the fact that the spectral characteristics of light scattering is different for air molecules and cloud. The result of Skyview's algorithm showed about 77% agreement between the observed cloud amount and the calculated, for the error range, the difference between calculated and observed cloudiness, within ${\pm}2$. Seasonally, the best accuracy of about 83% was obtained within ${\pm}2$ range in summer when the cloud amounts are higher, thus better signal-to-noise ratio. Furthermore, as the sky turbidity increased, the error also increased because of increased scattering which can explain the large error in spring. The algorithm still need to be improved in classifying sky condition more systematically with other complimentary instruments to discriminate thin cloud from haze to reduce errors in detecting clouds.