An improved SIFT algorithm based on adaptive threshold canny

Abstract

The traditional SIFT algorithm is popular to extract the feature points of target objects, but it also brings the feature points of non-target objects together, leading to mismatching. This paper proposes an improved SIFT algorithm based on adaptive threshold canny operator. In the proposed method, since it has the advantages of accurate edge detection and anti-noise ability, the adaptive threshold canny operator is first employed to detect the edges of an image, and then we can find the feature points by SIFT in the peripheral region of the edges. By introducing the adaptive threshold canny operator, the target objects can be separated from background, largely increasing the matching rate of SIFT algorithm. Experimental results demonstrate that, compared with the traditional SIFT and SURF algorithms, the proposed method can improve the robustness of feature points and further increase the matching rate, meanwhile reducing the cost of running time in a certain extent.