Feature matching is one of the most important research topics in the field of image processing. However

most available methods fail to achieve satisfying quantitative and qualitative matches simultaneously. In this study

we introduced epipolar constraint into speeded-up robust features (SURF) feature matching

thereby achieving significant improvement. In this method

the SURF algorithm was adopted to detect the feature points of each studied image. Then

the fundamental matrix was calculated using random sample consensus (RANSAC) and was used to obtain the epipolars of all the points. Finally

a constraint was introduced into the epipolars to filter error matches. Consequently

significantly improved matches with enhanced quantity and quality were achieved. The experimental results indicate that compared with the old method

our method cannot only obtain matches with high accuracy but can further achieve an increase of twofold to eightfold in quantity. The process and implementation of the proposed method are simple and accurate. Moreover

the method can increase the number of correct matches and handle different types of images.