A wide baseline matching method based on scale invariant feature descriptor

Abstract

Image matching is a fundamental aspect of many problems in computer vision. We describe a novel wide baseline matching method based on scale invariant feature descriptor. First, corners in image pairs are detected based on an improved Curvature Scale-Space (CSS) technique. These corners are relatively invariant to affine transformations, and are represented by using Scale Invariant Feature Transform (SIFT) descriptor to provide robust matching. The nearest neighbor distance is then applied to remove mismatched corners. Finally, the robust estimation algorithm, RANSAC, is adopt to estimate the fundamental matrix from the correspondence, and at the same time identify inlying matches. Experiments demonstrate the feasibility of this method.