• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.1
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• pp.156-161
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• 2009

This paper proposes a new method about performance improvement of soccer robots system by the revision of lens distortion most commonly occurred in camera and the revision of position and angle error in robot patch for the realization of robot position. Among the lens distortions, we revise geometrical distortion and apply it to soccer robots system for realtime environment. Patch used in the recognition and the distinction for coordination and direction of robot occurs a position and angle error according to the figure of it. In this paper, we suggest the method of reduction for position and angle error of robot by improved patch and verify its propriety through the experiment.

• Journal of Sensor Science and Technology
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• v.14 no.2
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• pp.109-115
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• 2005

Calibration is a prerequisite procedure for employing a camera as a 3D sensor in an automated machines like robots. As accurate sensing is possible only when the vision sensor is calibrated accurately, many different approaches and models have been proposed for increasing calibration accuracy. Particularly an important factor which greatly affects the calibration accuracy is the nonlinearity in the mapping between 3D world and corresponding 2D image. In this paper GMDH algorithm is used to learn the nonlinearity without physical modelling. The technique proposed can be effective in various situations where the levels of noises and characteristics of nonlinear distortion are different. In simulations and an experiment, the proposed technique showed good and reliable results.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.198-207
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• 1999

This paper presents an accurate algorithm for geometric calibration of X-ray imaging system. Calibration is a very important process for improving an imaging system performance. There has been a lot of previous works using linear camera modeling technique, where lens distortion is neglected and/or center of distortion is assumed to be known. Geometrical distortion of image intensifier, however, is very large and its center of distortion should be calculated. This paper presents a new calibration method to estimate the intensifier position and orientation, scale factor, distortion coefficient, magnification factor, and center of distortion using the least square method. We investigate the properties of the algorithm by computer simulation. Simulation results show that the parameters can be estimated accurately using the proposed algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.114-123
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• 2014

Vehicle Black Box (Event Data Recorder EDR) only recognizes the general surrounding environments of load. In addition, general EDR is difficult to recognize the images of a sudden illumination change. It appears that the lens is being a severe distortion. Therefore, general EDR does not provide the clues of the circumstances of the accident. To solve this problem, we estimate the value of Normalized Luminance Descriptor(NLD) and Normalized Contrast Descriptor(NCD). Illumination change is corrected using Normalized Image Quality(NIQ). Second, we are corrected lens distortion using model of Field Of View(FOV) based on designed method of fisheye lens. As a result, we propose integration algorithm of two methods that correct distortions of images using each Gamma Correction and Lens Correction in parallel.

• Journal of Sensor Science and Technology
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• v.13 no.1
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• pp.27-34
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• 2004

Camera calibration is an important and fundamental procedure for the application of a vision sensor to 3D problems. Recently many camera calibration methods have been proposed particularly in the area of robot vision. However, the reliability of data used in calibration has been seldomly considered in spite of its importance. In addition, a camera model can not guarantee good results consistently in various conditions. This paper proposes methods to overcome such uncertainty problems of data and camera models as we often encounter them in practical camera calibration steps. By the use of the RANSAC (Random Sample Consensus) algorithm, few data having excessive magnitudes of errors are excluded. Artificial neural networks combined in a two-step structure are trained to compensate for the result by a calibration method of a particular model in a given condition. The proposed methods are useful because they can be employed additionally to most existing camera calibration techniques if needed. We applied them to a linear camera calibration method and could get improved results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.2
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• pp.173-180
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• 2012

The most recent, 80 mega pixels digital camera appeared through the development of digital technology, and nonmetric digital cameras have been using in various field of photogrammetry. In this study, we experimented lens calibration using aerial photographs and ground control points. The aerial photographs were taken a non-metric digital camera which is CMOS(Complementary Metal Oxide Semiconductor) 21.1 mega pixels sensor and 35mm lens at a helicopter. And the ground control points were selected on the 1:1,000 plotting origin data. As a result, we calculated focal length, PPA(Principal Point of Autocollimation) and symmetric radial distortion coefficients from the lens. Also, RMSE(root mean square error) and maximum residual of the ground control points from the aerial triangulation were compared before and after calibration. And we found that the accuracy of the after calibration was improved very significantly.

• Applied Microscopy
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• v.35 no.4
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• pp.98-104
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• 2005

A FOV (field-of-view) of the HV-MSC (high voltage multi-scan CCD, $1024{\times}1024$ pixels) camera mounted in the post-column HV-GIF (high voltage gatan image filter) has been drastically enlarged by the projection lens current adjustment. An imaging area of the HV-MSC camera obtained at the lowest magnification (2,000x) is $112{\mu}m^2$ which corresponds to the recording area of the film at the magnification of 8,800x, while the achievable recording area is only $0.43{\mu}m^2$ at the same magnification without this technique. Ignoring the image distortion of less than 5%, we have designed an on-site reference graph to estimate projection lens currents for microscope magnifications above 8,800x, where the recording area on the HVMSC is same as that on the film.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.90-103
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• 1999

In this paper, we present an overall algorithm for real-time camera parameter extraction which is one of key elements in implementing virtual studio. The prevailing mechanical methode for tracking cameras have several disadvantage such as the price, calibration with the camera and operability. To overcome these disadvantages we calculate camera parameters directly from the input image using computer-vision technique. When using zoom lenses, it requires real time calculation of lens distortion. But in Tsai algorithm, adopted for camera calibration, it can be calculated through nonlinear optimization in triple parameter space, which usually takes long computation time. We proposed a new method, separating lens distortion parameter from the other two parameters, so that it is reduced to nonlinear optimization in one parameter space, which can be computed fast enough for real time application.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.3
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• pp.224-229
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• 2007

This paper presents a camera calibration method using several images for three dimensional measurement applications such as stereo systems, mobile robots, and visual inspection systems in factories. Conventional calibration methods that use single image suffer from errors related to reference point extraction in image, lens distortion, and numerical analysis of nonlinear optimization. The camera parameter values obtained from images of same camera is not same even though we use same calibration method. The camera parameters that are obtained from several images of different view for a calibration target is usaully not same with large error values and we can not assume a special probabilistic distribution when we estimate the parameter values. In this paper, the median value of camera parameters from several images is used to improve estimation of the camera values in an iterative step with nonlinear optimization. The proposed method is proved by experiments using real images.