• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.42-42
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• 2017

Prediction of rice yield during a growing season would be very helpful to magnify rice yield as it also allows better farm practices to maximize yield with greater profit and lesser costs. UAV imagery based automatic detection of rice can be a relevant solution for early prediction of yield. So, we propose an image processing technique to predict rice yield using low altitude UAV images. We proposed $L^*a^*b^*$ color space based image segmentation algorithm. All images were captured using UAV mounted RGB camera. The proposed algorithm was developed to find out rice grain area from the image background. We took RGB image and applied filter to remove noise and converted RGB image to $L^*a^*b^*$ color space. All color information contain in both $a^*$ and $b^*$ layers and by using k-mean clustering classification of these colors were executed. Variation between two colors can be measured and labelling of pixels was completed by cluster index. Image was finally segmented using color. The proposed method showed that rice grain could be segmented and we can recognize rice grains from the UAV images. We can analyze grain areas and by estimating area and volume we could predict rice yield.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.192-198
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• 2020

The 3D point cloud is a key technology of object detection for virtual reality and augmented reality. In order to apply various areas of object detection, it is necessary to obtain 3D information and even color information more easily. In general, to generate a 3D point cloud, it is acquired using an expensive scanner device. However, 3D and characteristic information such as RGB and depth can be easily obtained in a mobile device. GNN (Graph Neural Network) can be used for object detection based on these characteristics. In this paper, we have generated RGB and RGBD by detecting basic information and characteristic information from the KITTI dataset, which is often used in 3D point cloud object detection. We have generated RGB-GNN with i-GNN, which is the most widely used LiDAR characteristic information, and color information characteristics that can be obtained from mobile devices. We compared and analyzed object detection accuracy using RGBD-GNN, which characterizes color and depth information.

• Journal of Animal Environmental Science
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• v.7 no.2
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• pp.103-110
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• 2001

This study conducted to monitor decomposition process of the charomatophore pollution solution of an animal slurry by using a CCD camera. After the solution was put into test tube, the images(R, G, B, H, L, S) values of the solution were measured by the imgae processing system, and those of it\`s optical density were measured for three hours to be decomposed by microscopic organism. The values of measured for three hours to be decomposed by microscopic organism. The values of measured images(R, G, B, H, L, S) were analysed and compared with those of the optical density. Some of the results are as follows. 1. High correlation coefficients, which analyzed by using data on linear equations, were 0.9557 and 0.9672. They were decreased regularly in this R-value experiment of RGB level. The microscopic organism in this experiment was effective for decomposition of the red charomatophore pollution solution. 2. The values of all correlation coefficients from relationship between RGB-value and optical density were more than 0.95 except H-values. RGB-values, which were average values of summed R, G, B values, had correlation coefficients of 0.9863, 0.9937. These results showed so good relationship that decomposition process of charomatophore pollution solution could be monitored by a image processing system.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.252-259
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• 2022

Artificial intelligence is used in fusion with image processing techniques using cameras. Image processing technology is a technology that processes objects in an image received from a camera in real time, and is used in various fields such as security monitoring and medical image analysis. If such image processing reduces the accuracy of recognition, providing incorrect information to medical image analysis, security monitoring, etc. may cause serious problems. Therefore, this paper uses a mixture of YOLOv4-tiny model and image processing algorithm and uses the COCO dataset for learning. The image processing algorithm performs five image processing methods such as normalization, Gaussian distribution, Otsu algorithm, equalization, and gradient operation. For RGB images, three image processing methods are performed: equalization, Gaussian blur, and gamma correction proceed. Among the nine algorithms applied in this paper, the Equalization and Gaussian Blur model showed the highest object detection accuracy of 96%, and the gamma correction (RGB environment) model showed the highest object detection rate of 89% outdoors (daytime). The image binarization model showed the highest object detection rate at 89% outdoors (night).

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.501-507
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• 2012

Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. In this paper, we explain a study on natural user interface (NUI) in human hand motion recognition using RGB color information and depth information by Kinect camera from Microsoft Corporation. To achieve the goal, hand tracking and gesture recognition have no major dependencies of the work environment, lighting or users' skin color, libraries of particular use for natural interaction and Kinect device, which serves to provide RGB images of the environment and the depth map of the scene were used. An improved Camshift tracking algorithm is used to tracking hand motion, the experimental results show out it has better performance than Camshift algorithm, and it has higher stability and accuracy as well.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.309-311
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• 2012

본 논문에서는 3차원 입체 비디오처리 기술의 최종목표인 디지털 홀로그램을 생성하는데 필요한 정보인 객체의 좌표와 색상정보를 얻기 위해서 간단하게 장면이 정확히 일치하는 RGB image와 depth image를 생성할 수 있는 시스템을 구축하는 방법을 제안하였다. 가시광선과 적외선의 파장을 이용하여 파장에 따라 투과율이 달라지는 cold mirror를 사용하여 같은 시점에 대한 RGB + depth image를 얻은 후, 전처리 과정에서 카메라 왜곡에 대한 lens correction과정을 걸친 후, 해상도를 맞추기 위한 resolution resize과정을 마친 후, 디지털 홀로그램으로 구현 할 object를 추출한다. 그 다음 CGH(computer-generated hologram) 알고리즘으로 추출한 object를 CGH로 만든다.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.19-26
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• 2008

In this paper, we converted an RGB into an YCbCr image input from CCD camera and then after compute difference two consecutive images, conduct Glassfire Labeling. We extract an image become ware of motion-change, if the difference between most broad(area) and Area critical value more than critical value. We enforce the detection of facial characteristics to an extracted motion-change images by using AdaBoost algorithm to extract an characteristics.

• Current Optics and Photonics
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• v.5 no.1
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• pp.32-39
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• 2021

We propose a three-dimensional (3D) streaming system based on a lamina display that can convey field information in real-time by creating floating 3D images that can satisfy the accommodation cue. The proposed system is mainly composed of three parts, namely: a 3D vision camera unit to obtain and provide RGB and depth data in real-time, a 3D image engine unit to realize the 3D volume with a fast response time by using the RGB and depth data, and an optical floating unit to bring the implemented 3D image out of the system and consequently increase the sense of presence. Furthermore, we devise the streaming method required for implementing augmented reality (AR) images by using a multilayered image, and the proposed method for implementing AR 3D video in real-time non-face-to-face communication has been experimentally verified.

• Current Optics and Photonics
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• v.6 no.3
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• pp.304-312
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• 2022

Digital images of cultural relics captured using line scan cameras present limitations due to uneven intensity and low contrast. To address this issue, this report proposes a colour linear array image enhancement method that can maintain a constant colour. First, the colour linear array image is converted from the red-green-blue (RGB) colour space into the hue-saturation-intensity colour space, and the three components of hue, saturation, and intensity are separated. Subsequently, the hue and saturation components are held constant while the intensity component is processed using the established intensity compensation model to eliminate the uneven intensity of the image. On this basis, the contrast of the intensity component is enhanced using an improved local contrast enhancement method. Finally, the processed image is converted into the RGB colour space. The experimental results indicate that the proposed method can significantly improve the visual effect of colour linear array images. Moreover, the objective quality evaluation parameters are improved compared to those determined using existing methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.361-364
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• 2022

본 논문에서는 스마트 기기를 사용할 시 자세가 거북목 자세인지 아닌지 판별하는 시스템을 제안한다. 거북목 증후군이란 목이 구부정하게 앞으로 나오는 자세를 오래 취해 목이 일자목으로 바뀌고 뒷목, 어깨, 허리 등에 통증이 생기는 증상을 말하며, 수술이나 약물치료보다 평소의 자세 습관을 고치는 방법이 효과적이다. 기존의 연구들은 노트북에 내장되어있는 웹캠을 이용한 CNN기반의 학습모델은 영상의 명도와 학습 데이터 등에 많은 영향을 받고 학습 데이터를 모을 때 초상권 문제로 수집이 어렵다. 본 논문에서는 이러한 문제를 예방하고자 Openpose 오픈 소스를 이용한 뼈대를 기반으로 측면에서의 앉은 자세를 한습 모델로 실시간 검증하여, 거북목 자세인지 아닌지를 효율적이고 정확하게 판별한다.