Wide baseline stereo matching based on scale invariant feature transformation with hybrid geometric constraints. Issue 6 (1st December 2014)

• Record Type: Journal Article
• Authors: Yang, Huachao; Yu, Mei; Zhang, Shubi
• Is Part Of: IET computer vision. Volume 8:Issue 6(2014)
• Page Start: 611
• Page End: 619
• Publication Date: 2014-12-01
• Subjects: image matching -- stereo image processing -- geometry; wide baseline stereo matching -- hybrid geometric constraints -- geometric deformations -- illumination changes -- scale invariant feature transformation algorithm -- SIFT algorithm -- hybrid matching scheme -- feature-based methods -- area-based methods -- SIFT-based robust weighted least squares matching -- LSM method -- 2D projective transformation -- normalised cross correlation metric -- adaptive scale -- SIFT features -- SIFT-NCC -- SIFT-LSM -- robust matching propagation -- epipolar geometry -- local homography constraints -- geometrical consistency checking -- feature-based SIFT matching method -- 3D space -- epipolar constraint -- bidirectional selection strategy -- error matches; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2013.0265
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Wide baseline stereo matching is a challenging task because of the presence of significant geometric deformations and illumination changes within the images. Based on the scale invariant feature transformation (SIFT) algorithm, this study proposes a new hybrid matching scheme that uses both the feature‐based and the area‐based methods to find reliable matches from sparse to dense under different geometric constraints. Firstly, the authors propose a SIFT‐based robust weighted least squares matching (LSM) method modelled by a two‐dimensional (2D) projective transformation to establish the initial correspondences and their local homographies. In this method, a normalised cross correlation metric modified with an adaptive scale and an orientation of the SIFT features (SIFT‐NCC) is proposed to find a good initial alignment for the SIFT‐LSM. Secondly, a robust matching propagation using the SIFT‐NCC starts from the initial matches under an epipolar geometry and the local homography constraints; geometrical consistency checking is used simultaneously to identify the false matches. Thirdly, they use an improved, feature‐based SIFT matching method to find the correspondences from the points that are not coplanar in the 3D space under an epipolar constraint only. A bidirectional selection strategy is used to remove the error matches.