• Journal of the Optical Society of Korea
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• v.20 no.1
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• pp.140-149
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• 2016

For visual measurement under dynamic scenarios, a zoom lens camera is more flexible than a fixed one. However, the challenges of distortion prediction within the whole focal range limit the widespread application of zoom lens cameras greatly. Thus, a novel sequential distortion correction method for a zoom lens camera is proposed in this study. In this paper, a distortion assessment method without coupling effect is depicted by an elaborated chessboard pattern. Then, the appropriate distortion correction model for a zoom lens camera is derived from the comparisons of some existing models and methods. To gain a rectified image at any zoom settings, a global distortion correction modeling method is developed with bundle adjustment. Based on some selected zoom settings, the optimized quadratic functions of distortion parameters are obtained from the global perspective. Using the proposed method, we can rectify all images from the calibrated zoom lens camera. Experimental results of different zoom lens cameras validate the feasibility and effectiveness of the proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.125-131
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• 1999

In this paper, we address practical methods for calculating camera lens distortion for real time applications. Although the lens distortion problem can be easily ignored for constant-parameter lenses, in the field of real-time camera calibrations, for zoom lenses a large number of calculations are needed to calculate the distortion. However, if the distortion can be calculated independently of the other camera parameter, we can easily calibrate a camera without the need for a large number of calculations. Based on Tsai's camera model, we propose two different methods for calculating lens distortion. These methods are so simple and require so few calculations that the lens distortion can be rapidly calculated even in real-time applications. The first method is to refer to the focal length - lens distortion Look Up Table(LUT), which is constructed in the initialization process. The second method is to use the relationship between the feature points found in the image. Experiments were carried out for both methods, results of which show that the proposed methods are favorably comparable in performance with non-real full optimization method.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.475-483
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• 2015

This study examined the feasibility of using an automatic lens distortion correction (ALDC) camera as the payload for a photogrammetric unmanned aerial vehicle (UAV) system. First, lens distortion for the interior orientation (IO) parameters was estimated. Although previous studies have largely ignored decentering distortion, this study revealed that more than 50% of the distortion of the ALDC camera was caused by decentering distortion. Second, we compared the accuracy of bundle adjustment for camera calibration using three image types: raw imagery without the ALDC option; imagery corrected using lens profiles; and imagery with the ALDC option. The results of image triangulation, the digital terrain model (DTM), and the orthoimage using the IO parameters for the ALDC camera were similar to or slightly better than the results using self-calibration. These results confirm that the ALDC camera can be used in a photogrammetric UAV system using only self-calibration.

• 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.179-185
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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 varios degrees of decentering,that is,the optical centers of lens elements are not strictly collinear. Thin prism distortion arises form 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 and to apply the line of part manufacturing.

• 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 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.

• 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 Broadcast Engineering
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• v.14 no.3
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• pp.299-310
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• 2009

In this paper, we proposed a new method to correct the zoom lens distortion for the video sequence captured by the zoom lens. First, we defined the nonlinear zoom lens distortion model which is represented by the focal length and the lens distortion using the characteristic that lens distortion parameters are nonlinearly and monotonically changed while the focal length is increased. Then, we chose some sample images from the video sequence and estimated a focal length and a lens distortion parameter for each sample image. Using these estimated parameters, we were able to optimize the zoom lens distortion model. Once the zoom lens distortion model was obtained, lens distortion parameters of other images were able to be computed as their focal lengths were input. The proposed method has been made experiments with many real images and videos. As a result, accurate distortion parameters were estimated from the zoom lens distortion model and distorted images were well corrected without any visual artifacts.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.267-277
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• 2019

Photogrammetry and computer vision are identical in determining the three-dimensional coordinates of images taken with a camera, but the two fields are not directly compatible with each other due to differences in camera lens distortion modeling methods and camera coordinate systems. In general, data processing of drone images is performed by bundle block adjustments using computer vision-based software, and then the plotting of the image is performed by photogrammetry-based software for mapping. In this case, we are faced with the problem of converting the model of camera lens distortions into the formula used in photogrammetry. Therefore, this study described the differences between the coordinate systems and lens distortion models used in photogrammetry and computer vision, and proposed a methodology for converting them. In order to verify the conversion formula of the camera lens distortion models, first, lens distortions were added to the virtual coordinates without lens distortions by using the computer vision-based lens distortion models. Then, the distortion coefficients were determined using photogrammetry-based lens distortion models, and the lens distortions were removed from the photo coordinates and compared with the virtual coordinates without the original distortions. The results showed that the root mean square distance was good within 0.5 pixels. In addition, epipolar images were generated to determine the accuracy by applying lens distortion coefficients for photogrammetry. The calculated root mean square error of y-parallax was found to be within 0.3 pixels.