• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2923-2925
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• 1999

The objective of camera calibration is to determine the internal optical characteristics of camera and 3D position and orientation of camera with respect to the real world. Calibration procedure applicable to general purpose cameras and lenses. The general method to revise the accuracy rate of calibration is using mathematical distortion of lens. The effective og calibration show big difference in proportion to distortion of camera lens. In this paper, we propose the method which calibration distortion model by using neural network. The neural network model implicity contains all the distortion model. We can predict the high accuracy of calibration method proposed in this paper. Neural network can set properly the distortion model which has difficulty to estimate exactly in general method. The performance of the proposed neural network approach is compared with the well-known Tsai's two stage method in terms of calibration errors. The results show that the proposed approach gives much more stable and acceptabke calibration error over Tsai's two stage method regardless of camera resolution and camera angle.

• 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 Institute of Control, Robotics and Systems
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• v.18 no.9
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• pp.819-822
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• 2012

This paper describes an on-line magnetic distortion calibration procedure for a magnetometer. The horizontal magnetic field is calculated through the earth magnetic field sensed by 3-axes magnetometer. The ellipse equation is derived from a set of horizontal magnetic field data using least square method and calibration parameters are determined. The calibration process is performed iteratively until parameters are not renewed, and experimental results show the effectiveness of the devised method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.510-514
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• 1994

This paper deals with an application of neural network to camera calibration with wide angle lens and 2-D range finding. Wide angle lens has an advantage of having wide view angles for mobile environment recognition ans robot eye in hand system. But, it has severe radial distortion. Multilayer neural network is used for the calibration of the camera considering lens distortion, and is trained it by error back-propagation method. MLP can map between camera image plane and plane the made by structured light. In experiments, Calibration of camers was executed with calibration chart which was printed by using laser printer with 300 d.p.i. resolution. High distortion lens, COSMICAR 4.2mm, was used to see whether the neural network could effectively calibrate camera distortion. 2-D range of several objects well be measured with laser range finding system composed of camera, frame grabber and laser structured light. The performance of 3-D range finding system was evaluated through experiments and analysis of the results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.56-68
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• 1994

The objective of camera calibration is to determine the internal optical characteristics of camera and the three-dimensional position and orientation of camera with respect to the real world. Calibration procedure for computer vision should be automatical, accurate and applicable to general purpose cameras and lenses. In this paper, we present camera calibration method which meets the above requirements. The algorithm is based on the two-stage method which takes into account lens distortion in the second stage. In this paper, the overdetermined nonlinear system is established in terms of the constraints to all directions and our calibration algorithm is proposed which is constructed by using Marquardt iterations and our calibration algorithm is proposed which is constructed by using Marquardt iteration method in solving nonlinear equations. Experimental results indicate that lens distortion should be taken into consideration for the calibration of the general-purpose lens. With 24 calibration points acquired out of 512$\times$512 image, the proposed algorithm came up with average error of less than 1 pixel and showed a higher accuracy over the conventional two-stage method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.225-229
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• 1997

This paper describes the camera calibration based-neural network with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. Radial distortion causes and inward or outward displacement of a given image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. Thin prism distortion arises from imperfection in lens design and manufacturing as well as camera assembly. It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing. The performance of proposed camera calibration is illustrated by simulation and experiment.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.65-69
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• 1997

This paper presents a new approach to the calibration of a SCARA robot orientation with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. Radial distortion causes an inward or outward displacement of a given image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. Thin prism distortion arises from imperfection in lens design and manufacturing as well as camera assembly. It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1082-1095
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• 2019

Camera calibration is an important part of machine vision and close-range photogrammetry. Since current calibration methods fail to obtain ideal internal and external camera parameters with limited computing resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial distortion and tangential distortion models to establish the camera model with nonlinear distortion items. Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method can quickly obtain high-precise internal and external camera parameters. The experimental results show that the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds (0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel. Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.2
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• pp.11-16
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• 2001

This paper presents an in self-calibration method to corrent the Z-directional distortion of AFM(Atomic Force Microscopy).

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1836-1839
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• 1997

This paper presents machine vision technique with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. Radial distortion causes an inward or outward displacement of a given image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing.