Cai Bolin, Wang Yuwei, Wang Keyi, Ma Mengchao, Chen Xiangcheng
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China.
Department of Instrument Science and Opto-Electronics Engineering, Hefei University of Technology, Hefei 230088, China.
Sensors (Basel). 2017 Oct 16;17(10):2361. doi: 10.3390/s17102361.
Camera parameters can't be estimated accurately using traditional calibration methods if the camera is substantially defocused. To tackle this problem, an improved approach based on three phase-shifting circular grating (PCG) arrays is proposed in this paper. Rather than encoding the feature points into the intensity, the proposed method encodes the feature points into the phase distribution, which can be recovered precisely using phase-shifting methods. The PCG centers are extracted as feature points, which can be located accurately even if the images are severely blurred. Unlike the previous method which just uses a single circle, the proposed method uses a concentric circle to estimate the PCG center, such that the center can be located precisely. This paper also presents a sorting algorithm for the detected feature points automatically. Experiments with both synthetic and real images were carried out to validate the performance of the method. And the results show that the superiority of PCG arrays compared with the concentric circle array even under severe defocus.
如果相机存在严重的散焦问题,那么使用传统的校准方法就无法准确估计相机参数。为了解决这个问题,本文提出了一种基于三相移圆形光栅(PCG)阵列的改进方法。该方法不是将特征点编码到强度中,而是将特征点编码到相位分布中,通过相移方法可以精确恢复该相位分布。提取PCG中心作为特征点,即使图像严重模糊,这些特征点也能被精确地定位。与之前仅使用单个圆的方法不同,该方法使用同心圆来估计PCG中心,从而可以精确地定位中心。本文还提出了一种自动对检测到的特征点进行排序的算法。通过合成图像和真实图像进行实验,以验证该方法的性能。结果表明,即使在严重散焦的情况下,PCG阵列也比同心圆阵列具有优越性。