Exploration of the Fusion of FAST Algorithm and SIFT Algorithm in Image Feature Extraction

In the field of computer vision, image feature extraction is the bridge connecting low-level image data with high-level visual tasks such as image matching and object recognition. The SIFT algorithm occupies an important position in feature extraction due to its outstanding stability and uniqueness, but its computational complexity is relatively high, limiting its application in scenarios with high real-time requirements, and the algorithm runs relatively slowly. In contrast, the FAST algorithm is known for its efficiency, but it does not provide a method for calculating feature descriptors and lacks invariance to scale and rotation changes, only suitable for translational image changes. Therefore, this paper explores the fusion of the FAST algorithm and the SIFT algorithm, aiming to combine their strengths.