Find in Library

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.