基于仿射高斯尺度空间理论，提出了一种完全仿射不变特征 (FAIF)提取算法。 FAIF算法针对仿射高斯尺度空间难以构造的问题，提出了将仿射高斯尺度空间转化为尺度空间的思路。以图像特征区域的协方差矩阵作为特征区域各向异性程度的度量，将各向异性的特征区域通过旋转压缩的方式变换为各向同性区域，最后在各向同性区域上提取完全仿射不变特征点。实验结果表明， FAIF算法可以适应大的视角和尺度变换，并且在立体场景的图片中也有足够多的匹配点，其性能优于现有的仿射不变特征提取算法。

A Fully Affine Invariant Feature (FAIF) detector based on affine Gaussian scale-space has been put forward. Affine Gaussian scale-space is difficult to be built up. FAIF transforms the affine Gaussian scale-space into scale space to cope with the complexity of affine Gaussian scale-space construction. Covariance matrix of an image patch is used to measure the isotropy of the patch. Anisotropic patches are transformed into isotropic ones by rotating and squeezing. Finally, the fully affine invariant key points are detected on isotropic patches. Experimental results indicate that FAIF has the ability to cope with large view angle and scale changes. Moreover, sufficient matches have been detected by FAIF even in images of 3D scenes. Compared with the state- of- the- art, FAIF is the best.