Abstract
Image feature extraction and matching is an essential work in computer image processing. This technique aligns two or more images of the same scene obtained by different sensors under different or the same imaging conditions to determine the relationship between them. Based on the SURF algorithm, this paper adopts the local two-dimensional entropy to improve the uniqueness of the extracted feature points. Then the Euclidean distance in the traditional algorithm is changed to the Manhattan distance, and the distance evaluation function is introduced for pre-processing. Finally, the particle swarm algorithm is applied to optimize the feature point matching process. Through theoretical analysis and experimental contrast, the improved SURF algorithm has significantly improved both matching speed and accuracy, demonstrating the algorithm's reliability and effectiveness.
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