Mathematical Analysis of Image Matching Techniques

arXiv:2604.07574v1 Announce Type: new Abstract: Image matching is a fundamental problem in Computer Vision with direct applications in robotics, remote sensing, and geospatial data analysis. We present an analytical and experimental evaluation of classical local feature-based image matching algorithms on satellite imagery, focusing on the Scale-Invariant Feature Transform (SIFT) and the Oriented FAST and Rotated BRIEF (ORB). Each method is evaluated through a common pipeline: keypoint detection, descriptor extraction, descriptor matching, and geometric verification via RANSAC with homography estimation. Matching quality is assessed using the Inlier Ratio - the fraction of correspondences consistent with the estimated homography. The study uses a manually constructed dataset of GPS-annotated satellite image tiles with intentional overlaps. We examine the impact of the number of extracted keypoints on the resulting Inlier Ratio.

On the Uphill Battle of Image frequency Analysis
Bag of Bags: Adaptive Visual Vocabularies for Genizah Join Image Retrieval
The Weaponization of Computer Vision: Tracing Military-Surveillance Ties through Conference Sponsorship
WUTDet: A 100K-Scale Ship Detection Dataset and Benchmarks with Dense Small Objects
ChangeBridge: Spatiotemporal Image Generation with Multimodal Controls for Remote Sensing

Source: arXiv cs.CV | 2026-04-10

Related