• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.1
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• pp.34-40
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• 2005

In this paper, we describe the modeling for the 3D robot laser scanning system consisting of a laser stripe projector, camera, and 5-DOF robot and propose its calibration method. Nonlinear radial distortion in the camera model is considered for improving the calibration accuracy. The 3D range data is calculated using the optical triangulation principle which uses the geometrical relationship between the camera and the laser stripe plane. For optimal estimation of the system model parameters, real-coded genetic algorithm is applied in the calibration process. Experimental results show that the constructed system is able to measure the 3D position within about 1mm error. The proposed scheme could be applied to the kinematically dissimilar robot system without losing the generality and has a potential for recognition for the unknown environment.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.5
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• pp.371-381
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• 2021

A machine vision inspection system consists of a camera, optics, illumination, and image acquisition system. Especially a scanning system has to be made to measure a large inspection area. Therefore, a machine vision line scan camera needs a line scan light source. A line scan light source should have a high light intensity and a uniform intensity distribution. In this paper, an offset calibration and slope calibration methods are introduced to obtain a uniform light intensity profile. Offset calibration method is to remove the deviation of light intensity among channels through adding intensity difference. Slope calibration is to remove variation of light intensity slope according to the control step among channels through multiplying slope difference. We can obtain an improved light intensity profile through applying offset and slope calibration simultaneously. The proposed method can help to obtain clearer image with a high precision in a machine vision inspection system.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.803-811
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• 2002

Machine vision systems are usually used to identify traffic lanes and then determine the steering angle of an autonomous vehicle in real time. The steering angle is calculated using a geometric model of various parameters including the orientation, position, and hardware specification of a camera in the machine vision system. To find the accurate values of the parameters, camera calibration is required. This paper presents a new camera-calibration algorithm using known traffic lane features, line thickness and lane width. The camera parameters considered are divided into two groups: Group I (the camera orientation, the uncertainty image scale factor, and the focal length) and Group II(the camera position). First, six control points are extracted from an image of two traffic lines and then eight nonlinear equations are generated based on the points. The least square method is used to find the estimates for the Group I parameters. Finally, values of the Group II parameters are determined using point correspondences between the image and its corresponding real world. Experimental results prove the feasibility of the proposed algorithm.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.350-356
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• 2020

Workers have been replaced by mobile manipulators for factory automation in recent years. One of the typical tasks for automation is that a mobile manipulator moves to a target location and picks and places an object on the worktable. However, due to the pose estimation error of the mobile platform, the robot cannot reach the exact target position, which prevents the manipulator from being able to accurately pick and place the object on the worktable. In this study, we developed an automatic alignment system using a low-cost camera mounted on the end-effector of a collaborative robot. Camera calibration and pose estimation methods were also proposed for the automatic alignment system. This algorithm uses a markerboard composed of markers to calibrate the camera and then precisely estimate the camera pose. Experimental results demonstrate that the mobile manipulator can perform successful pick and place tasks on various conditions.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.161-167
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• 2001

Camera calibration is very important problem in 3D measurement using vision system. In this paper is proposed the simple method for camera calibration. It is designed that uses the principle of vanishing points and the concept of corresponding points extracted from the parallel line pairs. Conventional methods are necessary for 4 reference points in one frame. But we proposed has need for only 2 reference points to estimate vanishing points. It has to calculate camera parameters, focal length, camera attitude and position. Our experiment shows the validity and the usability from the result that absolute error of attitude and position is in $10^{-2}$.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.4
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• pp.867-873
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• 1999

Camera calibration is a procedure which calculates internal and external parameters of a camera with the Down world coordinates of the control points. Accurate camera calibration is required for achieving accurate visual measurements. In this paper, we propose a simple and flexible camera calibration using neural networks which doesn't require a special knowledge of 3D geometry and camera optics. There are some applications which are not in need of the values of the internal and external parameters. The proposed method is very useful to these applications. Also, the proposed camera calibration has advantage that resolves the ill-condition as object plane is near parallel image plane. The ill-condition is frequently met in product inspection. For little more accurate calibration, acquired image is divided into two regions according to radial distortion of lens and neural network is applied to each region. Experimental results and comparison with Tsai's algorithm prove the validity of the proposed camera calibration.

• Journal of Sensor Science and Technology
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• v.28 no.3
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• pp.182-186
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• 2019

When a camera is employed for 3D sensing, accurate camera calibration is vital as it is a prerequisite for the subsequent steps of the sensing process. Camera calibration is usually performed by complex mathematical modeling and geometric analysis. On the other contrary, data learning using an artificial neural network can establish a transformation relation between the 3D space and the 2D camera image without explicit camera modeling. However, a neural network requires a large amount of accurate data for its learning. A significantly large amount of time and work using a precise system setup is needed to collect extensive data accurately in practice. In this study, we propose a two-step neural calibration method that is effective when only a small amount of learning data is available. In the first step, the camera projection transformation matrix is determined using the limited available data. In the second step, the transformation matrix is used for generating a large amount of synthetic data, and the neural network is trained using the generated data. Results of simulation study have shown that the proposed method as valid and effective.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.6
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• pp.537-542
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• 2015

The measurement of 3D shape is important in inspecting the quality of product. In this paper, we present a 3D shape measurement system of fastener using a camera and a slit laser. Calibration structure with slits is used in the extrinsic calibration of the camera and laser. The pose of the camera and laser is computed under the same world coordinate system in the calibration structure. Reflection of laser light on the metal surface causes many difficulties in the robust detection of them on image. We overcome this difficulty by using color and dynamic programming. Motor stage is used to rotate the fastener to recover the whole 3D shape of the surface of it.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.6
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• pp.139-144
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• 2014

As a result of a mismatch of the residual stress between both sides of the silicon wafer, which warps and distorts during the patterning process. The accuracy of the warpage measurement is related to the calibration. A CCD camera was used for the calibration. Performing optimization of the error function constructed with phase values measured at each pixel on the CCD camera, the coordinates of each light source can be precisely determined. Measurement results after calibration was performed to determine the warpage of the silicon wafer demonstrate that the maximum discrepancy is $5.6{\mu}m$ with a standard deviation of $1.5{\mu}m$ in comparison with the test results obtained by using a Form TalySurf instrument.