依据已设计完成的基于同心球透镜的四镜头多探测器阵列拼接成像系统, 对该系统图像拼接配准过程所采用的特征检测提取、特征向量匹配与筛选、空间变换模型参数估计等算法进行了研究。首先, 采用Fast-Hessian检测子提取参考图像和待配准图像的特征点, 并生成加速鲁棒特征(SURF)描述向量。接着, 采用快速近似最近邻(FANN)逼近搜索算法获得初始的匹配点对, 并对匹配点对特征向量的欧式距离进行排序。然后, 参照成像系统光学设计参数设定合理的阈值, 筛选并保留下较好的匹配点对。最后, 提出了一种改进的渐进式抽样一致性(IPROSAC)算法对空间变换矩阵模型进行参数估计, 从而得到参考图像与待配准图像的空间几何变换关系。实验结果表明: 该算法对图像尺寸、旋转和光照变化都具有一定的不变性, 特征匹配时间为0542 s, 配准变换时间0031 s, 配准误差精度小于01 pixel, 可以满足成像系统关于图像配准实时性和准确性的要求, 具有一定的工程应用价值。

According to the detector arrays mosaic imaging system designed with four lenses based on concentric spherical lens, its applied algorithms about the image registration is investigated, such as feature detection and extraction, feature vector matching and screening, spatial transformation model and parameter estimation, etc. First, the fast-hessian detection algorithm is used to find features, and generate feature vector of SURF descriptors. Second, the fast approximate nearest neighbor search algorithm is used to obtain the initial matching points and to sort the Euclidean distance between feature vectors in the matching points. Then after screening the feature points, the good ones are preserved based on a reasonable threshold interval from the optical design parameters. Finally, the transform parameters are estimated by using the improved progressive sample consensus method and the spatial geometry transformation relationship is obtained about the reference image and registration image. Experimental results indicate that the algorithm has some invariance about the size, rotation and illumination changes; the feature matching time is 0542 s, and the registration transform time is 0031 s; the registration error precision is less than 01 pixel, which can meet the requirements of the imaging system about the image registration including good real-time and accuracy performance.