Abstract:

[Objective] In order to improve the registration accuracy and authenticate the wrapping paper images, fine-grained registration is performed on the cigarette packaging images by using the matching feature points extracted from the optimized SIFT (Scale-invariant feature transform) algorithm. [Methods] After performing block processing on the images, removing unstable feature points, and using coarse registration of the homography matrix to filtrate paired points by distance constraint, an evaluation approach is proposed based on average distance betwteen fine-grained matching pairs to improve the registration performance based on SIFT features. [Results] The experimental results show that the improved feature point extraction method in this paper can extract more balanced feature points and improve the estimated matching rate. The proposed registration evaluation standard can effectively evaluate the registration quality, and the coarse registered matching points can improve the accuracy of fine-grained registration of the image and authenticate the wrapping paper images. [Limitations] The current improvement is focused on the selection of matching pairs and there is still room for improvement in the research of fine-grained registration methods. [Conclusions] Experiments prove that this strategy can improve registration accuracy and achieve the purpose of authenticating cigarette wrapping paper images.

Key words: SIFT, feature detection, feature matching, homography transformation, image registration