• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1270-1275
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• 2013

A lot of research about camera calibration and lens distortion for wide-angle lens has been made. Especially, calibration for fish-eye lens which has 180 degree FOV(field of view) or above is more tricky, so existing research employed a huge calibration pattern or even 3D pattern. And it is important that calibration parameters (such as distortion coefficients) are suitably initialized to get accurate calibration results. It can be achieved by using manufacturer information or lease-square method for relatively narrow FOV(135, 150 degree) lens. In this paper, without any previous manufacturer information, camera calibration and barrel undistortion for fish-eye lens with over 180 degree FOV are achieved by only using one calibration pattern image. We applied QR decomposition for initialization and Regularization for optimization. With the result of experiment, we verified that our algorithm can achieve camera calibration and image undistortion successfully.

• 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 Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2007.04a
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• pp.161-164
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• 2007

The paper studies in the wide-angle lens and distortion tendency and employs the correction techniques suitable to the fish-eye lens using the existing photographic survey methods. After carrying out the calibration of the the fish-eye lens, we calculated the correction parameters, and then developed the method that convert the original image-point to new image-point correcting distortion. The objectives of suggested calibration method in this paper are to calibrate the image of the the fish-eye lens used in the computer-vision and the control-instrumentation field widely. The proposed technique expects to improve the accuracy of the image of the fish-eye lens in the indoor tracking and monitoring field. Also the referenced cross point auto-extraction program is embodied for improving efficiency of the lens correction techniques. Consequently, this calibration method would be applied to the automated distorting correction method on not only the fish-eye lens also general lens.

• 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 Internet Computing and Services
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• v.6 no.5
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• pp.57-71
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• 2005

Precise visual measurement applications, like video measuring system (VMS), use camera systems with motorized zoom lens for fast and efficient measurement. In this paper, we introduce an efficient calibration method for zoom lens of VMS controlled by servo motor. For the automated zoom lens calibration of the VMS, only zoom lens setting needs to be calibrated and parameters calibrated by zoom lens settings are image center and pixel size changed by zoom levels, The extrinsic parameters, like focus and iris, do not need to be calibrated since the parameters are usually fixed, It needs a lot of time and effort to calibrate the camera for ali the different zoom levels. In this paper, we also propose an efficient and fast zoom lens calibration method, which calculates the calibration parameters of the zoom lens settings for the minimum number of zoom levels and estimates other parameters for the uncalculated zoom levels using the interpolation of the calculated parameter values.

• 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 Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.16-22
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• 2012

In this paper, we present spiral pattern which suits for optical simulator to calibrate fisheye lens and compensate geometric distortion. Using spiral pattern, we present calibration without mathematical modeling in advance. Proposed spiral pattern used to input image of optical simulator. Using fisheye lens image, we calibrate a fisheye lens by matching geometrically moved dots to corresponding original dots which leads not to need mathematical modeling. Proposed algorithm calibrates using dot matching which matches spiral pattern image dot to distorted image dot. And this algorithm does not need modeling in advance so it is effective. Proposed algorithm is enabled at processing of pattern recognition which has to get the exact information using fisheye lens for digital zooming. And this makes possible at compensation of geometric distortion and calibration of fisheye lens image applying in various image processing.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.592-594
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• 2004

Camera calibration is an important problem to determine the relationship between 3D real world and 2D camera image. The existing calibration methods can be classified into linear and non-linear models. The linear methods are simple and robust against noise, but the accuracy expectation is generally poor. In comparison, if the non-linearity, which is due mainly to lens distortion, is corrected, the accuracy can be better. However, as the optical features of lens are diverse, no non-linear method can be always effective for diverse vision systems. In this paper, we propose a new approach to correct the calibration error of a linear method using GMDH algorithm. The proposed technique is simple in concept and showed improved accuracy in various cases.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.596-601
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• 2000

In a robot/vision system, the vision sensor, typically a CCD array sensor, is mounted on the robot hand. The problem of determining the relationship between the camera frame and the robot hand frame is refered to as the hand-eye calibration. In the literature, various methods have been suggested to calibrate camera and for sensor registration. Recently, one-step approach which combines camera calibration and sensor registration is suggested by Horaud & Dornaika. In this approach, camera extrinsic parameters are not need to be determined at all configurations of robot. In this paper, by modifying the camera model and including the lens distortion effect in the perspective transformation matrix, a new one-step approach is proposed in the hand-eye calibration.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.250-255
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• 2003

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.