• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.9
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• pp.4472-4477
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• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.422-428
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• 2008

In this paper, we propose the robust object detection algorithm with spatial gradient information. To do this, first, we eliminate error values that appear due to complex environment and various illumination change by using prior methods based on hue and intensity from the input video and background. Visible shadows are eliminated from the foreground by using an RGB color model and a qualified RGB color model. And unnecessary values are eliminated by using the HSI color model. The background is removed completely from the foreground leaving a silhouette to be restored using spatial gradient and HSI color model. Finally, we validate the applicability of the proposed method using various indoor and outdoor conditions in a complex environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.4
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• pp.595-604
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• 2013

Currently, registered number of imported vehicles is increasing rapidly over the years. Accordingly, environment improvements of vehicle maintenance company for maintenance of luxury vehicle such as imported vehicle are continuously being made. In this paper, we propose a key frame extraction method based on HSV color model for smart vehicle management system implementation to offer for customer reliability of maintenance vehicle. After automatically recognize the license plates of the vehicle using vehicle license plate recognition system when the vehicle come in the car center, we check the repair history and request of the vehicle based on it. We implement mobile services which provide extracted key frame images to the user after extract key frames from vehicle repair video. In addition, we verify the superiority of key frame extraction method by applying a smart vehicle management system. Finally, we convert the RGB color to HSV color to improve the performance of proposed key frame extraction scheme. As a result, we confirmed that our scheme is more excellence about 30% in terms of recall than RGB color model from the performance evaluations.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.35-42
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• 2021

In this paper, we propose a deep learning-based person re-identification method using a three-dimensional RGB-Depth Xtion2 camera considering joint coordinates and dynamic features(velocity, acceleration). The main idea of the proposed identification methodology is to easily extract gait data such as joint coordinates, dynamic features with an RGB-D camera and automatically identify gait patterns through a self-designed one-dimensional convolutional neural network classifier(1D-ConvNet). The accuracy was measured based on the F1 Score, and the influence was measured by comparing the accuracy with the classifier model (JC) that did not consider dynamic characteristics. As a result, our proposed classifier model in the case of considering the dynamic characteristics(JCSpeed) showed about 8% higher F1-Score than JC.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.83-98
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• 2021

Urban green space is an important component for enhancing urban ecosystem health. Thus, identifying the spatial structure of urban green space is required to manage a healthy urban ecosystem. The Ministry of Environment has provided the level 3 land cover map(the highest (1m) spatial resolution map) with a total of 41 classes since 2010. However, specific urban green information such as street trees was identified just as grassland or even not classified them as a vegetated area in the map. Therefore, this study classified detailed urban green information(i.e., tree, shrub, and grass), not included in the existing level 3 land cover map, using two types of high-resolution(<1m) remote sensing data(i.e., airborne LiDAR and RGB ortho imagery) in Suwon, South Korea. U-Net, one of image segmentation deep learning approaches, was adopted to classify detailed urban green space. A total of three classification models(i.e., LRGB10, LRGB5, and RGB5) were proposed depending on the target number of classes and the types of input data. The average overall accuracies for test sites were 83.40% (LRGB10), 89.44%(LRGB5), and 74.76%(RGB5). Among three models, LRGB5, which uses both airborne LiDAR and RGB ortho imagery with 5 target classes(i.e., tree, shrub, grass, building, and the others), resulted in the best performance. The area ratio of total urban green space(based on trees, shrub, and grass information) for the entire Suwon was 45.61%(LRGB10), 43.47%(LRGB5), and 44.22%(RGB5). All models were able to provide additional 13.40% of urban tree information on average when compared to the existing level 3 land cover map. Moreover, these urban green classification results are expected to be utilized in various urban green studies or decision making processes, as it provides detailed information on urban green space.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1603-1613
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• 2017

This paper proposes a method for effectively segmenting the hand region using a widely popular RGB color-based webcam. This performs the empirical preprocessing method four times to remove the noise. First, we use Gaussian smoothing to remove the overall image noise. Next, the RGB image is converted into the HSV and the YCbCr color model, and global fixed binarization is performed based on the statistical value for each color model, and the noise is removed by the bitwise-OR operation. Then, RDP and flood fill algorithms are used to perform contour approximation and inner area fill operations to remove noise. Finally, ROI (hand region) is selected by eliminating noise through morphological operation and determining a threshold value proportional to the image size. This study focuses on the noise reduction and can be used as a base technology of gesture recognition application.

• Electronics and Telecommunications Trends
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• v.31 no.4
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• pp.36-43
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• 2016

실 환경에 존재하는 모든 것을 3차원 모델로 쉽게 복원할 수 있을 것이라는 생각과 원격지에 있는 환경과 사람을 같은 공간에 있는 듯 상호작용할 수 있게 된 것은 그리 오래되지 않았다. 이는 일정 해상도를 보장해주는 RGB-D 센서의 개발과 이러한 센서들을 사용한 3차원 복원 관련 연구들이 활발히 수행되면서 가능하게 되었다. 본고에서는 널리 쓰이고 있는 RGB-D 카메라를 사용하여 실시간으로 때로는 온라인상에서 3차원으로 복원하고 가시화하는 기술에 대하여 살펴보고자 한다. 하나 또는 여러 개의 RGB_D 카메라를 사용하거나 모바일 장치에 장착된 RGB-D 센서를 사용하여 넓은 공간, 움직이는 사람, 온라인 상태의 환경 등을 실시간으로 복원하기 위한 기술들을 세부적으로 설명한다. 또한, 최근에 발표된 기술들이 다루고 있는 이슈들을 설명하고 향후 3차원 복원기술의 연구개발 방향에 대해서 논의한다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.309-314
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• 2014

To generate a complete 3D model from depth images of multiple RGB-D cameras, it is necessary to find 3D transformations between RGB-D cameras. This paper proposes a convenient view calibration technique using a spherical object. Conventional view calibration methods use either planar checkerboards or 3D objects with coded-pattern. In these conventional methods, detection and matching of pattern features and codes takes a significant time. In this paper, we propose a convenient view calibration method using both 3D depth and 2D texture images of a spherical object simultaneously. First, while moving the spherical object freely in the modeling space, depth and texture images of the object are acquired from all RGB-D camera simultaneously. Then, the external parameters of each RGB-D camera is calibrated so that the coordinates of the sphere center coincide in the world coordinate system.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.281-284
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• 2019

본 논문은 CNN (Convolution Neural Networks)의 첫 번째 컨볼루션층(convolution layer)을 RGB-csb(RGB channel separation block)로 대체하여 입력 영상의 RGB 값을 특징 맵에 적용시켜 정확성을 제고시킬 수 있는 선행연구 결과에 추가적으로, 훈련 및 시험 영상 수에 따른 분석을 통하여 정확도 향상방법을 제안한다. 제안한 방법은 영상의 개수가 작을수록 각 학습 간의 정확도 편차가 크게 나타나는 불안정성은 있지만 기존 CNN모델에 비하여 정확도 차이가 증가함을 알 수 있다.