改进仿射尺度不变特征变换算法的图像配准

Improved ASIFT algorithm for image registration

为了更好地处理匹配效率、重复纹理匹配和仿射不变性匹配等问题,对完全仿射不变特征变换(ASIFT)算法进行两方面改进。匹配框架中特征提取的改进提高了ASIFT算法的匹配效率;利用优化随机采样算法(ORSA)结合以单应矩阵为几何线性约束模型的随机抽样一致性(RANSAC)改进匹配算法,提高了匹配精度和重复纹理结构的适应能力。实验结果表明,提出的改进算法能较好地匹配高度相似纹理,计算量小,计算速度快且精度高。

Image registration is a well researched topic of computer vision. To deal with matching efficiency, repetitive pattern matching and affine invariant matching better, two improvements over the state-of-the-art Affine-Scale Invariant Feature Transform （ASIFT） algorithm were presented. The feature extraction of matching frame was developed to improve the matching efficiency of the ASIFT algorithm. The second increased the accuracy of matching and the adaptive capacity of repetitive patterns through the use of improved matching algorithm by combining Optimized Random Sample Consensus （ORSA） with Random Sample Consensus （RANSAC） algorithm based on geometric linear constraint model with homography matrix. The experimental results show that the proposed method is able to well match highly repetitive patterns and has smaller calculation, faster speed and higher accuracy as well.