Scale-Adaptive Feature Extraction Using OpenCV

Abstract

Image stitching is a dynamic and evolving area that merges multiple photos of the same subject to create a seamless, high-resolution panoramic image. It is a crucial component of computer vision as well as computer graphics. This paper focuses on the feature-based paradigm, which is useful for finding prominent features in images in order to create meaningful correspondences. This approach leverages advanced algorithms such as SURF (Speeded-Up Robust Features) and RANSAC (Random Sample Consensus) to detect key points and estimate geometric transformations between image pairs, respectively. By combining the feature-based method with the SURF and RANSAC algorithms in a strategic way, picture stitching systems can handle issues with viewpoint, scale, and content variations and produce an intuitive mix of panoramic imagery.