Scale-invariant feature matching based on pairs of feature points

On the basis of feature points pairing, a scale-invariant feature matching method is proposed in this study. The distance between two features is used to compute feature pairs´ support region size, which is different from the methods using detectors to provide information to find the support region. Moreover, to achieve rotation invariance, a sub-region division method based on intensity order is introduced. For comparison to the popular descriptors scale-invariant feature transform and speeded-up robust features, the authors also choose the detected points by difference of Gaussian and fast Hessain detectors as feature points to start the authors´ method. Additional experiments compare the reported method with similar proposed methods, such as Tell´s and Fan´s. The experimental results show that the authors´ proposed descriptor outperforms the popular descriptors under various image transformations, especially on images with scale and viewpoint transformations.