摘要： 鱼眼镜头视角大,但由鱼眼镜头组成的鱼眼摄像机拍摄的图片具有严重的畸变,不利于人眼观察和机器识别。为此,基于已有的九点非迭代优化算法,提出一种改进算法以完成鱼眼自标定和自动校正,包括将最稳定极限区域与尺度不变特征变换算法结合以自动获取一对鱼眼图像的特征匹配点。利用核密度估计方法代替随机抽样一致性算法,实现鱼眼自标定,选择最优参数代入畸变模型中进行鱼眼图像畸变校正。在事先不知道场景信息和摄像机镜头参数的前提下,通过输入两幅有重合区域的图片自动匹配其特征点,从而获取鱼眼图像的校正。标定及校正结果表明,与原算法需要人为选择匹配点不同,提出的算法可自动获取特征匹配点,校正结果精确,为自动匹配并获取鱼眼图像的校正提供了可能。
关键词:
鱼眼镜头,
鱼眼图像,
核密度估计,
自标定,
畸变校正
Abstract: Though fish eye lens has a large field of view,the images taken with the fish eye camera composed of fish eye lens have serious distortion which is not conducive to the human eye observation and machine recognition.An improved algorithm is proposed based on the existing nine point non-iterative optimization algorithm to realize fish eye self calibration and automatic correction.The Maximally Stable Extremal Region(MSER) is combined with Scale Invariant Feature Transform(SIFT) algorithm to automatically get one pair of fish eye image feature matching points.Using the kernel density estimation as non-iterative method to replace the random sample consensus algorithm,it achieves fish eye self calibration and image distortion correction by applying the optimal parameter into the distortion model.Without the prior knowledge about the scene information and the camera lens parameters,the algorithm can automatically match feature points of the two images which have overlap regions to achieve fish eye image correction.Calibration and correction results show that different with the original algorithm which requires artificial selection of matching point,the
proposed algorithm can acquire the feature matching point automatically,and the calibration result is correct,this makes it possible to automatically match and acquire the fish eye image correction.
Key words:
fish eye lens,
fish eye image,
kernel density estimation,
self-calibration,
distortion correction
中图分类号: