A Fully Affine Invariant Feature Detector

This paper proposes a Fully Affine Invariant Feature (FAIF) detector which is based on affine Gaussian scale-space. The covariance matrix of Maximally Stable Extremal Region is interpreted as an isotropy measure of an image patch. A local anisotropic image patch can be supposed as an affine transformed isotropic image patch. Therefore, the affine deformation of a MSER can be estimated by its covariance matrix. According to affine Gaussian scale-space theory, filters must be compatible with local image structures. An anisotropic image patch should be smoothed by an elliptical Gaussian filter which is difficult to be constructed directly. In order to use circular Gaussian filters, FAIF transforms affine Gaussian scale-space into scale space by the way that rotating and compressing an anisotropic image region into an isotropic one. The fully affine invariant features are detected on isotropic image patches by Scale Invariant Feature Transform (SIFT) algorithm. Experimental results show that FAIF has much more matches than the state-of-the-art algorithms.