Matching Point Elimination Algorithm Based on RANSAC

In computer vision and image processing, feature matching accuracy is critical for various applications, such as image stitching and object recognition. This paper focuses on optimizing the classic Scale-Invariant Feature Transform (SIFT) algorithm, which, despite its effectiveness in feature extraction, often encounters mismatched points during the matching process, affecting overall accuracy. To address this issue, we integrate the Random Sample Consensus (RANSAC) algorithm with SIFT. RANSAC iteratively estimates model parameters and eliminates mismatched points from the initial SIFT matches. Experimental results demonstrate that the combined approach significantly improves matching accuracy, reducing errors caused by noise or repetitive patterns in images. The improved algorithm shows notable performance in real-world scenarios, ensuring more reliable results for tasks requiring high precision. This method not only boosts matching accuracy but also enhances the robustness of the feature matching process, making it applicable for advanced fields such as medical imaging, autonomous vehicles, and augmented reality. By refining the feature matching process, this paper contributes to the development of more reliable computer vision systems that can operate effectively in complex environments with a high degree of precision.