A wide baseline matching method based on scale invariant feature descriptor

Jun Miao; Jun Chu; Guimei Zhang; Ruina Feng

doi:10.1117/12.832419

30 October 2009 A wide baseline matching method based on scale invariant feature descriptor

Jun Miao, Jun Chu, Guimei Zhang, Ruina Feng

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 749626 (2009) https://doi.org/10.1117/12.832419
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Jun Miao, Jun Chu, Guimei Zhang, and Ruina Feng "A wide baseline matching method based on scale invariant feature descriptor", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749626 (30 October 2009); https://doi.org/10.1117/12.832419

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available