• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.233-236
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• 2004

In this study, we analyzed the accuracy of the conversion from DLT parameters to physical camera parameters and optimized the use of DLT model for non-metric cameras in photogrammetric tasks. Using the simulated data, we computed two sets of physical camera parameters from DLT parameters and Bundle adjustment for various cases. Comparing two results based on the RMSE values of check points, we optimized the arrangement of GCPs for DLT.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.3
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• pp.283-291
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• 2011

In case of metric cameras, not only fiducial marks but also various parameters related to camera lens are provided to users for the interior orientation process. The parameters have been acquired through precise camera calibration in laboratory by camera maker. But, in case of non-metric cameras, the interior orientation parameters should be determined in person by users through camera calibration with great number of control points. The interior orientation parameters of metric cameras are practically used for long time. But in case of non-metric cameras, the long-term stability of the interior orientation parameters have not been established. Generally, the interior orientation parameters of non-metric cameras are determined in every photogrammetric work. It's been an obstacle to use the non-metric camera in photogrammetric project because so many control points are required to get the interior orientation parameters. In this study, camera calibrations and photogrammetric observations using a non-metric camera have been implemented 25 times periodically for 6 months and the results have been analyzed. As a result, long-them stability of the interior orientation parameters of a non-metric camera is analyzed.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.2 s.32
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• pp.39-43
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• 2005

In this study, we analyzed the accuracy of the conversion from DLT parameters to physical camera parameters and optimized the use of DLT model for non-metric cameras in photogrammetric tasks. Using the simulated data, we computed two sets of physical camera parameters from DLT parameters and Bundle adjustment for various cases. Comparing two results based on the RMSE values of check points, we optimized the arrangement of GCPs for DLT.

• 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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.141-146
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• 1998

Currently virtual studio has used the cromakey method in which an image is captured, and the blue portion of that image is replaced by a graphic image or a real image. The replaced image must be changed according to the camera motion. This paper proposes a novel method to extract camera parameters using the recognition of pentagonal patterns which are painted on the blue screen. The corresponding parameters are position, direction and focal length of the camera in the virtual studio. At first, pentagonal patterns are found using invariant features of the pentagon. Then, the projective transformation of two projected images and the camera parameters are calculated using the matched points. Simulation results indicate that camera parameters are more easily calculated compared to the conventional methods.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.440-440
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• 2000

In general, camera calibration is consisted of Indoor and Outdoor system. In case of Indoor system, it was optimized experimental condition. However, Outdoor system is different camera parameters for each image that is compared to equaled position. That is, it imply that camera parameters are varied by an environment with light or impulse noise, etc. So we make use of Image sequence because that they provide the more information for each image. In addition to, we use Corresponding line because it has less error than Corresponding point. Corresponding line has also the more information. In this paper, we suggest on-line camera calibration method using the time-varying Image sequence and Corresponding line. So we calculate camera parameters with intrinsic and extrinsic parameters in On-line system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.81-81
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• 2000

We use camera to apply human vision system in measurement. To do that, we need to know about camera parameters. The camera parameters are consisted of internal parameters and external parameters. we can fix scale factor&focal length in internal parameters, we can acquire external parameters. And we want to use these parameters in automatically driven vehicle by using camera. When we observe an camera parameters in respect with that the external parameters are important parameters. We can acquire external parameter as fixing focal length&scale factor. To get lane coordinate in image, we propose a lane detection filter. After searching lanes, we can seek vanishing point. And then y-axis seek y-sxis rotation component(${\beta}$). By using these parameter, we can find x-axis translation component(Xo). Before we make stepping motor rotate to be y-axis rotation component(${\beta}$), '0', we estimate image coordinates of lane at (t+1). Using this point, we apply this system to Kalman filter. And then we calculate to new parameters whick make minimum error.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.123-132
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• 2021

To determine 3D(three-dimensional) positions using a stereo-camera in close-range photogrammetry, camera calibration to determine not only the interior orientation parameters of each camera but also the relative orientation parameters between the cameras must be preceded. As time passes after performing camera calibration, in the case of non-metric cameras, the interior and relative orientation parameters may change due to internal instability or external factors. In this study, to evaluate the stability of the stereo-camera, not only the stability of two single cameras and a stereo-camera were analyzed, but also the three-dimensional position accuracy was evaluated using checkpoints. As a result of evaluating the stability of two single cameras through three camera calibration experiments over four months, the root mean square error was ±0.001mm, and the root mean square error of the stereo-camera was ±0.012mm ~ ±0.025mm, respectively. In addition, as the results of distance accuracy using the checkpoint were ±1mm, the interior and relative orientation parameters of the stereo-camera were considered stable over that period.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.187-190
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• 2003

In this paper, we propose an extrinsic calibration method for a multi-view camera to get an optimal pose in 3D space. Conventional calibration algorithms do not guarantee the calibration accuracy at a mid/long distance because pixel errors increase as the distance between camera and pattern goes far. To compensate for the calibration errors, firstly, we apply the Tsai's algorithm to each lens so that we obtain initial extrinsic parameters Then, we estimate extrinsic parameters by using distance vectors obtained from structural cues of a multi-view camera. After we get the estimated extrinsic parameters of each lens, we carry out a non-linear optimization using the relationship between camera coordinate and world coordinate iteratively. The optimal camera parameters can be used in generating 3D panoramic virtual environment and supporting AR applications.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.70-74
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• 2008

In the field of camera motion research, it is widely held that the position (movement) and pose (rotation) of cameras are correlated and cannot be independently separated. A new equation based on inner product is proposed here to independently separate the position and pose. It is proved that the position and pose are not correlated and the equation is applied to estimation of the camera exterior parameters using a real image and 3D data.