Image Matching Algorithm Combining SIFT with SSDA Based on Compressed Sensing

摘要

Considering the disadvantages of massive calculation and slow speed of traditional Scale Invariant Feature Transform (SIFT) algorithm, we proposed an improved image matching method which combines Compressed Sensing (CS) algorithm. The method works as follows. Firstly, target images and images to be matched are preprocessed and compressed using compressed sensing technology. Then, image feature points are extracted in combination with SIFT algorithm. Finally, Sequential Similarity Detection Algorithm (SSDA) with adaptive threshold is used to fast search of image matching to find an optimal matching position, and then a matching image is obtained. Experimental results demonstrate that the method realizes fast, image matching, efficiently overcomes the shortcomings of heavy computation and low efficiency in the process of extracting image features, and guarantees the matching accuracy and efficiency, which meets the real-time requestments in machine vision system.