Extreme Feature Regions for Image Matching

Extreme feature regions are increasingly critical for many image matching applications on affine image-pairs. In this paper, we focus on the time-consumption and accuracy of using extreme feature regions to do the affine-invariant image matching. Specifically, we proposed novel image matching algorithm using three types of critical points in Morse theory to calculate precise extreme feature regions. Furthermore, Random Sample Consensus (RANSAC) method is used to eliminate the features of complex background, and improve the accuracy of the extreme feature regions. Moreover, the saddle regions is used to calculate the covariance matrix for image matching. Extensive experiments on several benchmark image matching databases validate the superiority of the proposed approaches over many recently proposed affine-invariant SIFT algorithms. CCS Concepts •Computing methodologies → Image processing; image-matching; random sample consensus; affine invariant;