• Journal of the Semiconductor & Display Technology
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• v.20 no.4
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• pp.6-9
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• 2021

The 3D industry is drawing attention for its applications in various markets, including architecture, media, VR/AR, metaverse, imperial broadcast, and etc.. The current feature of the architecture we are introducing is to make 3D models more easily created and modified than conventional ones. Existing methods for generating 3D models mainly obtain values using specialized equipment such as RGB-D cameras and Lidar cameras, through which 3D models are constructed and used. This requires the purchase of equipment and allows the generated 3D model to be verified by the computer. However, our framework allows users to collect data in an easier and cheaper manner using cell phone cameras instead of specialized equipment, and uses 2D data to proceed with 3D modeling on the server and output it to cell phone application screens. This gives users a more accessible environment. In addition, in the 3D modeling process, object classification is attempted through deep learning without user intervention, and mesh and texture suitable for the object can be applied to obtain a lively 3D model. It also allows users to modify mesh and texture through requests, allowing them to obtain sophisticated 3D models.

• Journal of Broadcast Engineering
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• v.25 no.4
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• pp.598-608
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• 2020

In this paper, we propose a color correction technique for images acquired through a multi-view camera system for acquiring a 3D model. It is assumed that the 3D volume is captured indoors, and the position and intensity of the light is constant over time. 8 multi-view cameras are used, and converging toward the center of the space, so even if the lighting is constant, the intensity and angle of light entering each camera may be different. Therefore, a color optimization function is applied to a color correction chart taken from all cameras, and a color conversion matrix defining a relationship between the obtained 8 images is calculated. Using this, the images of all cameras are corrected based on the standard color correction chart. This paper proposed a color correction method to minimize the color difference between cameras when acquiring an image using 8 cameras of 3D objects, and experimentally proved that the color difference between images is reduced when it is restored to a 3D image.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.803-806
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• 2020

The body shape measurement method using 3D images has been widely used due to the recent development of 3D measurement cameras and algorithms. Existing 3D imaging devices are expensive devices, and there is a limit to their universalization. Due to the recent spread of inexpensive 3D cameras and the development of various measurement methods, various possibilities are being shown. It is expected to have a great impact on the medical device market that requires accurate data collection. Various medical device products using artificial intelligence are emerging, and accurate data collection is the most important to develop accurate artificial intelligence algorithms. Collection equipment using 3D cameras is expected to act as a major factor in the development of artificial intelligence algorithms using 3D images.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.2
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• pp.91-99
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• 2009

In the field of geospatial information, 3D shape information has been considered as a frame data for GIS. Many kind of physical shape information is, especially, required for the works related with space, such as planning, maintenance, management, etc. Conventional photogrammetry was implemented under the conditions with expensive metric cameras and analytical plotters operated by experts. Nowadays, however, the metric cameras and analytical plotters are replaced by low price non-metric digital cameras and personal computers by virtue of the progress in digital photogrammetry. This study aims to investigate the technology to easily and promptly produce 3D shape information required in geospatial information system using close-range photogrammetry with non-metric digital cameras. As the results of this study, 3D shape of an experimental object was made out with a common compact digital camera and only a known length of a line component in the object and the accuracy of the dimension of 3D shape was analyzed to be less than one pixel.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.3
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• pp.184-189
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• 2016

Human appearance analysis is an important part of both eastern and western medicine fields, such as Sasang constitutional medicine, rehabilitation medicine, dental medicine, and etc. By the rapid growing of depth camera technology, 3D measuring becomes popular in many applications including medical area. In this study, the possibility of using depth cameras in asymmetry analysis of human appearance is examined. We introduce the development of 3D measurement system using 2 Microsoft Kinect depth cameras and fully automated asymmetry analysis algorithms based on computer vision technology. We compare the proposed automated method to the manual method, which is usually used in asymmetry analysis. As a measure of repeatability, standard deviations of asymmetry indices are examined by 10 times repeated experiments. Experimental results show that the standard deviation of the automated method (1.00mm for face, 1.22mm for body) is better than that of the manual method (2.06mm for face, 3.44mm for body) for the same 3D measurement. We conclude that the automated method using depth cameras can be successfully applicable to practical asymmetry analysis and contribute to reliable human appearance analysis.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1749-1752
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• 2005

For online high accurate reconstruction of an object from an visual information, a linear reconstruction method for multiple images is popular. Basically this method needs many cameras or many different screen shots from different view points. This method, however, has the benefit of less calculation and is adequate for a real time application by comparing other popular method. In this paper, online reconstruction system using more than three cameras is treated. An evaluation method of cameras' position, and of the number is derived for the linear reconstruction method. To decrease errors that are caused from skew of lens, positional error between corresponding points is taken into consideration on the evaluation. The proposed evaluation method enables estimation of the adequate number of cameras and then of feasible view locations. Additionally, repeating search of epipolar lines enables estimation of the hidden point. Comparing with result of an average error analysis, it was confirmed that the proposed methods works effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.559-575
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• 2023

3D human pose estimation is widely applied in various fields, including action recognition, sports analysis, and human-computer interaction. 3D human pose estimation has achieved significant progress with the introduction of convolutional neural network (CNN). Recently, several researches have proposed the use of multiview approaches to avoid occlusions in single-view approaches. However, as the number of cameras increases, a 3D pose estimation system relying on a CNN may lack in computational resources. In addition, when a single host system uses multiple cameras, the data transition speed becomes inadequate owing to bandwidth limitations. To address this problem, we propose a distributed real-time 3D pose estimation framework based on asynchronous multiple cameras. The proposed framework comprises a central server and multiple edge devices. Each multiple-edge device estimates a 2D human pose from its view and sendsit to the central server. Subsequently, the central server synchronizes the received 2D human pose data based on the timestamps. Finally, the central server reconstructs a 3D human pose using geometrical triangulation. We demonstrate that the proposed framework increases the percentage of detected joints and successfully estimates 3D human poses in real-time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.205-210
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• 2008

In previous paper, we introduced 3d acquisition system with CCD cameras, but it was not distinguished from other 3d acquisition system with PSD camera. In this paper, we introduce the 3d data acquisition system using by stereo method with two cameras, show the calibration method of two cameras, and present 3d acquisition methods. we tried to improve the accuracy of 3d data acquisition and implemented the proposed methods. According to the result, we found that the proposed algorithms can boost the accuracy highly against the previous works. The proposed methods are to remove distortion on input images and adjust z axis. We describes the performance in the result.

• Proceedings of the KSME Conference
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• 2002.08a
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• pp.511-512
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• 2002

A new stereoscopic PTV was developed using two CCD cameras, stereoscopic photogrammetry based on a 3D-PTV principle. The arrangement of the two cameras was based on angular position. The calibration of cameras and the pair-matching of the three-dimensional velocity vectors were based on Genetic Algorithm based 3D-PTV technique. The constructed Stereoscopic W technique was tested on the standard images of the impinged jet proposed by VSJ. The results on the turbulent properties of the jet obtained by the constructed system showed a good agreement with the original LES data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.587-588
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• 2022

In this paper, we propose a rigid body tracking method in 3D space using 2D passive marker images from multiple motion capture cameras. First, a calibration process using a chess board is performed to obtain the internal variables of individual cameras, and in the second calibration process, the triangular structure with three markers is moved so that all cameras can observe it, and then the accumulated data for each frame is calculated. Correction and update of relative position information between cameras. After that, the three-dimensional coordinates of the three markers were restored through the process of converting the coordinate system of each camera into the 3D world coordinate system, the distance between each marker was calculated, and the difference with the actual distance was compared. As a result, an error within an average of 2mm was measured.