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Abstract: Camera calibration is a fundamental and important step in many machine vision applications. For some practical situations, computing camera parameters from merely a single image is becoming increasingly feasible and significant. However, the existing single view based calibration methods have various disadvantages such as ignoring lens distortion, requiring some prior knowledge or special calibration environment, and so on. To address these issues, we propose a line-based camera calibration method with lens distortion correction from a single image using three squares with unknown length. Initially, the radial distortion coefficients are obtained through a non-linear optimization process which is isolated from the pin-hole model calibration, and the detected distorted lines of all the squares are corrected simultaneously. Subsequently, the corresponding lines used for homography estimation are normalized to avoid the specific unstable case, and the intrinsic parameters are calculated from the sufficient restrictions provided by vectors of homography matrix. To evaluate the performance of the proposed method, both simulative and real experiments have been carried out and the results show that the proposed method is robust under general conditions and it achieves comparable measurement accuracy in contrast with the traditional multiple view based calibration method using 2D chessboard target. [Copyright &y& Elsevier]

Abstract: Camera calibration plays an important role in the field of machine vision applications. The popularly used calibration approach based on 2D planar target sometimes fails to give reliable and accurate results due to the inaccurate or incorrect localization of feature points. To solve this problem, an accurate and robust estimation method for camera parameters based on RANSAC algorithm is proposed to detect the unreliability and provide the corresponding solutions. Through this method, most of the outliers are removed and the calibration errors that are the main factors influencing measurement accuracy are reduced. Both simulative and real experiments have been carried out to evaluate the performance of the proposed method and the results show that the proposed method is robust under large noise condition and quite efficient to improve the calibration accuracy compared with the original state. [Copyright &y& Elsevier]