Performance Comparison of RANSAC and Other Model Estimation Methods in Panoramic Image Mosaic

As computer vision technology advances, the significance of outlier processing algorithms increases across various applications. Despite this, there is a lack of comprehensive comparisons to assess the performance of these algorithms. This paper focuses on evaluating the principles, advantages, and disadvantages of three key models: RANSAC, least squares, and lmed. Through experimental testing in diverse scenarios, the study identifies the most suitable model for different contexts. Results indicate that RANSAC exhibits the highest robustness against outliers, while lmed performs effectively with a moderate number of outliers. These findings are crucial for selecting appropriate models tailored to specific applications, ultimately enhancing the quality of panoramic mosaic images. The comparative analysis presented in this paper aims to guide practitioners in choosing the best outlier processing techniques based on their specific requirements and application environments, thereby improving the robustness and accuracy of computer vision systems.