A Feature Detection and Matching Algorithm Based on Harris Algorithm

摘要

Harris corner detection algorithm can detect stable image feature points in the image. But there are some problems such as large amount of computation, low positioning accuracy, only detecting corner positions without feature descriptor, which limit the application of the algorithm. In order to solve these problems, a new image feature extraction algorithm is proposed in this paper, which firstly screens the image twice, then the Harris corner detection algorithm is used to detect the corner position, and the corner position is optimized to sub-pixel level by iterative algorithm. Finally, the rotation invariant fast extraction (RIFD) descriptor is used to represent the feature point information. The experimental results show that the proposed algorithm effectively overcomes the shortcomings of the Harris algorithm, it can quickly and accurately extract stable features in the image. It has great application prospects in many image matching systems.