I n this article, a self-calibration method is presented for self-calibrating camera lens distortion by using only the image correspondences of two views. Two images of a single object are related by the epipolar geometry, which can be described by a 3 × 3 singular matrix called Fundamental matrix. It captures all geometric information contained in two images. An optimization method is applied to minimize the epipolar distances of the two images by adjusting the camera lens radial distortion

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... fficient. The merit of the method is that it does not rely on any ground truth data. Simulation and experimental results are given to demonstrate the applicability of the method. It is well known that actual cameras and lenses sustain a variety of aberrations and, thus, do not obey the perfect pinhole camera model. Lens geometric distortion has to do with the position of image points on the image plane. As a result of several types of imperfections in the design and assembly of lenses residing within the camera optical system, the distor-tion-free pinhole model does not hold. For applications that need accurate camera models, the pinhole model needs to be replaced by models that take into account positional errors due to lens distortion. Traditional methods for lens distortion calibration require the ground truth data that is usually provided by precision calibration objects [1]- [6] . Recently, self-calibration of lens distortion coefficient without knowing the three-dimensional (3-D) coordinates of the calibration object caught the attention of researchers [7]-[10]. The approach of using the epipolar constraint to calibrate lens distortion without using any ground truth data is noticeably attractive [9] . A set of image points from two views of a camera were collected and matched. The image coordinates of these matched image points were then entered to a cost function that was basically a norm of the epipolar distances of these image points. An optimization algorithm was subsequently applied to minimize the cost