• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1248-1250
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• 1996

The objective of camera calibration is to obtain the correlation between camera image coordinate and 3-D real world coordinate. Most calibration methods are based on the camera model which consists of physical parameters of the camera like position, orientation, focal length, etc and in this case camera calibration means the process of computing those parameters. In this research, we suggest a new approach which must be very efficient because the artificial neural network(ANN) model implicitly contains all the physical parameters, some of which are very difficult to be estimated by the existing calibration methods. Implicit camera calibration which means the process of calibrating a camera without explicitly computing its physical parameters can be used for both 3-D measurement and generation of image coordinates. As training each calibration points having different height, we can find the perspective projection point. The point can be used for reconstruction 3-D real world coordinate having arbitrary height and image coordinate of arbitrary 3-D real world coordinate. Experimental comparison of our method with well-known Tsai's 2 stage method is made to verify the effectiveness of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.413-418
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• 2001

In this paper, we propose a new camera calibration method which is based on a fuzzy model instead of a physical camera model of the conventional method. The camera calibration is to determine the correlation between camera image coordinate and real world coordinate. The camera calibration method using a fuzzy model can not estimate camera physical parameters which can be obtained in the conventional methods. However, the proposed method is very simple and efficient because it can determine the correlation between camera image coordinate and real world coordinate without any restriction, which is the objective of camera calibration. With calibration points acquired out of experiments, 3-D real world coordinate and 2-D image coordinate are estimated using the fuzzy modeling method and the results of the experiments demonstrate the validity of the proposed method.

• Journal of Biosystems Engineering
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• v.24 no.2
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• pp.159-166
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• 1999

A system to extract continuously the real 3-D geometric fearture information from 2-D image of an object, which is fed randomly via conveyor has been developed. Two sets of structured laser lightings were utilized. And the laser structured light projection image was acquired using the camera from the signal of the photo-sensor mounted on the conveyor. Camera coordinate calibration matrix was obtained, which transforms 2-D image coordinate information into 3-D world space coordinate using known 6 points. The maximum error after calibration showed 1.5 mm within the height range of 103mm. The correlation equation between the shift amount of the laser light and the height was generated. Height information estimated after correlation showed the maximum error of 0.4mm within the height range of 103mm. An interactive 3-D geometric feature extracting software was developed using Microsoft Visual C++ 4.0 under Windows system environment. Extracted 3-D geometric feature information was reconstructed into 3-D surface using MATLAB.

• Journal of Biosystems Engineering
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• v.26 no.1
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• pp.21-28
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• 2001

Major deficiencies of current automation scheme including various robots for bioproduction include the lack of task adaptability and real time processing, low job performance for diverse tasks, and the lack of robustness of take results, high system cost, failure of the credit from the operator, and so on. This paper proposed a scheme that could solve the current limitation of task abilities of conventional computer controlled automatic system. The proposed scheme is the man-machine hybrid automation via tele-operation which can handle various bioproduction processes. And it was classified into two categories. One category was the efficient task sharing between operator and CCM(computer controlled machine). The other was the efficient interface between operator and CCM. To realize the proposed concept, task of the object identification and extraction of 3D coordinate of an object was selected. 3D coordinate information was obtained from camera calibration using camera as a measurement device. Two stereo images were obtained by moving a camera certain distance in horizontal direction normal to focal axis and by acquiring two images at different locations. Transformation matrix for camera calibration was obtained via least square error approach using specified 6 known pairs of data points in 2D image and 3D world space. 3D world coordinate was obtained from two sets of image pixel coordinates of both camera images with calibrated transformation matrix. As an interface system between operator and CCM, a touch pad screen mounted on the monitor and remotely captured imaging system were used. Object indication was done by the operator’s finger touch to the captured image using the touch pad screen. A certain size of local image processing area was specified after the touch was made. And image processing was performed with the specified local area to extract desired features of the object. An MS Windows based interface software was developed using Visual C++6.0. The software was developed with four modules such as remote image acquisiton module, task command module, local image processing module and 3D coordinate extraction module. Proposed scheme shoed the feasibility of real time processing, robust and precise object identification, and adaptability of various job and environments though selected sample tasks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.1
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• pp.162-172
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• 2014

A three-dimensional dynamic model for simulating various motions of full vehicle is presented. The model has 16 independent degrees of freedom (DOF) consisting of three kinds of components; a vehicle body of 6 DOF, 4 independent suspensions equipped at every corner of the body, and 4 tire models linked with each suspension. The dynamic equations are represented in six coordinate frames such as world fixed coordinate, vehicle fixed coordinate, and four wheel fixed coordinate frames. Then these lead to the approximated prediction model of vehicle posture. Both lateral and longitudinal dynamics can be computed simultaneously under the conditions of which various inputs including steering command, driving torque, gravity, rolling resistance of tire, aerodynamic resistance, etc. are considered. It is shown through simulations that the proposed 3D model can be useful for precise design and performance analysis of any full vehicle control systems.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.31-38
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• 2017

In this paper, we propose a system which estimates Manhattan coordinate systems for urban scene images using a convolutional neural network (CNN). Estimating the Manhattan coordinate system from an image under the Manhattan world assumption is the basis for solving computer graphics and vision problems such as image adjustment and 3D scene reconstruction. We construct a CNN that estimates Manhattan coordinate systems based on GoogLeNet [1]. To train the CNN, we collect about 155,000 images under the Manhattan world assumption by using the Google Street View APIs and calculate Manhattan coordinate systems using existing calibration methods to generate dataset. In contrast to PoseNet [2] that trains per-scene CNNs, our method learns from images under the Manhattan world assumption and thus estimates Manhattan coordinate systems for new images that have not been learned. Experimental results show that our method estimates Manhattan coordinate systems with the median error of $3.157^{\circ}$ for the Google Street View images of non-trained scenes, as test set. In addition, compared to an existing calibration method [3], the proposed method shows lower intermediate errors for the test set.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.824-833
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• 2006

In this paper, the image modelling of road's lane markings is established using view frustum(VF) model. From this model, a measurement system of lane markings and obstacles is proposed. The system also involve the real time processing of the 3D position coordinate and the distance data from the camera to the points on the 3D world coordinate by virtue of the camera calibration. In order to reduce their measurement error, an useful algorithm for which analyze the geometric variations due to traveling vehicle's fluctuation using VF model is proposed. In experiments, without correction, for instance, the $0.4^{\circ}$ of pitching rotation gives the error of $0.4{\sim}0.6m$ at the distance of 10m, but the more far distance cause exponentially the more error. We con finned that this algorithm can be reduced less than 0.1m of error at the same condition.

• Economic and Environmental Geology
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• v.48 no.6
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• pp.537-543
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• 2015

In Korea, the use of world geodetic system(WGS) has been mandated in year 2010. Accordingly, the national geographic information institute(NGIS) provides the digital maps according to the WGS. Nevertheless, most of the digital geological maps are still based on the Tokyo Datum(TD). Therefore, users should conduct 2D/3D geological spatial analysis after converting the coordinates of digital geological maps to WGS. The conversion process is often tedious and troublesome for certain users. Therefore, in this study, the method to transform coordinate from TD to WGS using ArcToolbox is introduced for users not familiar with the process. For a better appreciation, the Chungju and Hwanggang-ri digital sheets of 1:50,000 scale was chosen as an example. Here, Chungju and Hwanggang-ri sheets were defined based on the TD-central origin and TD-east origin, respectively. The two sheets were merged after the transformation of TD-east origin of Hwanggang-ri to the TD-central origin, and eventually transformed to WGS-central origin. The merged map was found to match exactly with the digital map(Daeso 367041). The problem of coordinate determination in previous digital geological maps was solved effectively. The proposed method is believed to be helpful to 2D/3D geological spatial analysis of various geological thematic maps.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.216-220
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• 2007

Camera calibration in machine vision is the process of determining the intrinsic camera parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• 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.