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Proceedings Paper

A wide baseline matching method based on scale invariant feature descriptor
Author(s): Jun Miao; Jun Chu; Guimei Zhang; Ruina Feng
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Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 749626 (30 October 2009); doi: 10.1117/12.832419
Show Author Affiliations
Jun Miao, Nanchang Hangkong Univ. (China)
Jun Chu, Nanchang Hangkong Univ. (China)
Guimei Zhang, Nanchang Hangkong Univ. (China)
Ruina Feng, Nanchang Hangkong Univ. (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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