Super‐speed up robust features image geometrical registration algorithm

Abstract

Speed up robust features (SURF) image geometrical registration algorithm available tends to have a one-to-many association problem in feature association. One feature point in an image is associated with multiple feature points in another image, of which some or even all are mismatching points. Coordinates of these mismatching points are used to compute transformation matrix, making it difficult to obtain desirable registration. To solve this problem of SURF algorithm, super-SURF image geometrical registration algorithm is proposed, in which information richness areas are selected to detect feature points and to implement feature points association. The degree of closeness of multiple feature points from the one-to-many feature point pairs is analysed in order to remove feature point pairs with larger errors and retain those with smaller errors. Transformation matrix is then determined with coordinates of feature point pairs retained. Registration image can be obtained after transformation of floating image. Experimental results indicate that super-SURF image geometrical registration algorithm is of higher matching accuracy with less running time than SURF algorithm.