• Proceedings of the KSME Conference
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• 2001.11b
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• pp.394-399
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• 2001

A new stereoscopic PIV is introduced. The system works with CCD cameras, stereoscopic photogrammetry, and a 3D-PTV principle. Virtual images are produced for the construction of a benchmark testing tool of PIV techniques. The arrangement of the two cameras is based on angular position. The calibration of cameras and the pair-matching of the three-dimensional velocity vectors are based on 3D-PTV technique.

• Journal of Broadcast Engineering
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• v.24 no.5
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• pp.765-774
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• 2019

In this paper, we propose a point cloud matching algorithm for multiple RGB-D cameras. In general, computer vision is concerned with the problem of precisely estimating camera position. Existing 3D model generation methods require a large number of cameras or expensive 3D cameras. In addition, the conventional method of obtaining the camera external parameters through the two-dimensional image has a large estimation error. In this paper, we propose a method to obtain coordinate transformation parameters with an error within a valid range by using depth image and function optimization method to generate omni-directional three-dimensional model using 8 low-cost RGB-D cameras.

• Proceedings of the KSME Conference
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• 2001.06e
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• pp.555-559
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• 2001

A new stereoscopic PIV is developed using two CCD cameras, stereoscopic photogrammetry, and a 3D-PTV principle. The wake of a circular cylinder is measured by the developed stereoscopic PIV technique. The B mode vortical structure of the wake over the Reynolds number 300 is clearly seen by the developed technique. The arrangement of the two cameras is based on angular position. The calibration of cameras and the pair-matching of the three-dimensional velocity vectors are based on 3D-PTV technique.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.746-754
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• 2008

A 3D stereoscopic image is generated by interdigitating every scene with video editing tools that are rendered by two cameras' views in 3D modeling tools, like Autodesk MAX(R) and Autodesk MAYA(R). However, the depth of object from a static scene and the continuous stereo effect in the view of transformation, are not represented in a natural method. This is because after choosing the settings of arbitrary angle of convergence and the distance between the modeling and those two cameras, the user needs to render the view from both cameras. So, the user needs a process of controlling the camera's interval and rendering repetitively, which takes too much time. Therefore, in this paper, we will propose the 3D stereoscopic image editing system for solving such problems as well as exposing the system's inherent limitations. We can generate the view of two cameras and can confirm the stereo effect in real-time on 3D modeling tools. Then, we can intuitively determine immersion of 3D stereoscopic image in real-time, by using the 3D stereoscopic image preview function.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.1-9
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• 2007

In this paper, we propose a new 3D modeling and rendering system using multiple stereo cameras. When target objects are captured by cameras, each capturing PC segments the objects and estimates disparity fields, then they transmit the segmented masks, disparity fields, and color textures of objects to a 3D modeling server. The modeling server generates 3D models of the objects from the gathered masks and disparity fields. Finally, the server generates a video at the designated point of view with the 3D model and texture information from cameras.

• Current Optics and Photonics
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• v.8 no.3
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• pp.246-258
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• 2024

In aerospace and energy engineering, the reconstruction of three-dimensional (3D) temperature distributions is crucial. Traditional methods like algebraic iterative reconstruction and filtered back-projection depend on voxel division for resolution. Our algorithm, blending deep learning with computer graphics rendering, converts 2D projections into light rays for uniform sampling, using a fully connected neural network to depict the 3D temperature field. Although effective in capturing internal details, it demands multiple cameras for varied angle projections, increasing cost and computational needs. We assess the impact of camera number on reconstruction accuracy and efficiency, conducting butane-flame simulations with different camera setups (6 to 18 cameras). The results show improved accuracy with more cameras, with 12 cameras achieving optimal computational efficiency (1.263) and low error rates. Verification experiments with 9, 12, and 15 cameras, using thermocouples, confirm that the 12-camera setup as the best, balancing efficiency and accuracy. This offers a feasible, cost-effective solution for real-world applications like engine testing and environmental monitoring, improving accuracy and resource management in temperature measurement.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.2
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• pp.94-103
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• 2004

The vision system model used for this study involves the six parameters that permits a kind of adaptability in that relationship between the camera space location of manipulable visual cues and the vector of robot joint coordinates is estimated in real time. Also this vision control method requires the number of cameras to transform 2-D camera plane from 3-D physical space, and be used irrespective of location of cameras, if visual cues are displayed in the same camera plane. Thus, this study is to investigate the optimal number of cameras used for the developed vision control system according to the change of the number of cameras. This study is processed in the two ways : a) effectiveness of vision system model b) optimal number of cameras. These results show the evidence of the adaptability of the developed vision control method using the optimal number of cameras.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.422-424
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• 2021

In this paper, it leads the approach of fusing multiple RGB cameras for visual objects recognition based on deep learning with convolution neural network and 3D Light Detection and Ranging (LiDAR) to observe the environment and match into a 3D world in estimating the distance and position in a form of point cloud map. The goal of perception in multiple cameras are to extract the crucial static and dynamic objects around the autonomous vehicle, especially the blind spot which assists the AV to navigate according to the goal. Numerous cameras with object detection might tend slow-going the computer process in real-time. The computer vision convolution neural network algorithm to use for eradicating this problem use must suitable also to the capacity of the hardware. The localization of classified detected objects comes from the bases of a 3D point cloud environment. But first, the LiDAR point cloud data undergo parsing, and the used algorithm is based on the 3D Euclidean clustering method which gives an accurate on localizing the objects. We evaluated the method using our dataset that comes from VLP-16 and multiple cameras and the results show the completion of the method and multi-sensor fusion strategy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.615-618
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• 2021

Recently, 3D reconstruction of real objects with multi-cameras has been widely used for many services such as VR/AR, motion capture, and plenoptic video generation. For accurate 3D reconstruction, geometry and color matching between multiple cameras will be needed. However, previous calibration and correction methods for geometry (internal and external parameters) and color (intensity) correction is difficult for non-majors to perform manually. In this paper, we propose a toolkit with procedural geometry calibration and color correction among cameras with different positions and types. Our toolkit consists of an easy user interface and turned out to be effective in setting up multi-cameras for reconstruction.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.439-448
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• 2020

In this paper, we propose a modified optimization algorithm for point cloud matching of multi-view RGB-D cameras. In general, in the computer vision field, it is very important to accurately estimate the position of the camera. The 3D model generation methods proposed in the previous research require a large number of cameras or expensive 3D cameras. Also, the methods of obtaining the external parameters of the camera through the 2D image have a large error. In this paper, we propose a matching technique for generating a 3D point cloud and mesh model that can provide omnidirectional free viewpoint using 8 low-cost RGB-D cameras. We propose a method that uses a depth map-based function optimization method with RGB images and obtains coordinate transformation parameters that can generate a high-quality 3D model without obtaining initial parameters.