Image Stitching Model Based on Homography Estimation

Abstract

This paper presents an improved system to join images. It solves viewpoint twists and edge errors by using two way homography repair. We use SIFT matching with fast library approximation to link features reliably. We apply simple matching methods to align left/right sequences. The process sets the image size by analyzing the furthest points. It blends images in three steps by mapping weights that know the projection. We warp images in layers using RANSAC to find homography with a 5.0px limit. We then reverse map features to keep distortions low. Tests show an 85.3 % average of inlier matches for 120 image pairs. They also yield a smooth blend (38.7dB PSNR in overlapping areas). The system runs in linear time by reusing existing features and warping images simultaneously. Comparison shows a 23 % drop in cumulative errors compared with step-by-step methods. This approach works well for unordered image sets with small viewpoint changes. Results prove the system is strong for building panoramas while keeping running time low.