Abstract
Abstract. This paper proposes a line-matching algorithm based on feature grouping and a line band descriptor (LBD) to address the insufficient reliability of individual line descriptors for line matching. First, the algorithm generates line-pairs according to geometrical relationships such as the distances and angles between line segments extracted from a single image. Subsequently, the algorithm employs the epipolar line of intersection between two lines in a reference line-pair to constrain candidate pairs corresponding to the reference line-pair. Thereafter, each line in the reference line-pair is considered individually, and its support region and the corresponding support region of each candidate line in the candidate pairs are established, following which an affine transformation is used for unifying the sizes of the reference support region and the candidate support region. Moreover, the LBD descriptor is then used for describing the reference and candidate lines. The Euclidean distances between the reference line and each candidate line descriptors are calculated, and the nearest neighbor distance ratio (NNDR) is used as a criterion for determining the final matching. Finally, the one-to-many and many-to-one line correspondences in matching results are transformed into one-to-one line correspondences by fitting multiple lines to the new line; simultaneously, incorrect matches are eliminated. The experimental results show that the proposed algorithm yields reliable line-matching performance for close-range images.
Highlights
Feature matching is at the core of computer vision research and has been widely used in three-dimensional (3D) reconstruction, image registration and retrieval, image merging, and other such fields (Taylor et al, 1992; David et al, 2005; Chandraker et al, 2009)
Based on the line segments detected using the line segment detector (LSD) algorithm (Grompone Von Gioi et al, 2010) and on the corresponding points obtained using the scale-invariant feature transform (SIFT) algorithm, which utilizes random sample consensus (RANSAC), our algorithm consists of the following five steps: (1) Generate line-pairs in each image using the permutation and combination function model and remove meaningless pairs according to the geometrical relationships, such as the distance and angle between two lines
Thereafter, the corresponding support regions based on the overlap of the two lines are constructed, and an affine transformation is used for unifying the sizes of both support regions
Summary
Feature matching is at the core of computer vision research and has been widely used in three-dimensional (3D) reconstruction, image registration and retrieval, image merging, and other such fields (Taylor et al, 1992; David et al, 2005; Chandraker et al, 2009). This algorithm makes use of the advantage of line-pairs and the stability of the LBD In this method, line-pairs are generated according to the geometrical relationships between constituent lines; for each line-pair, the epipolar line of the intersection point is used for reducing the search space from two-dimensional to onedimensional. Line-pairs are generated according to the geometrical relationships between constituent lines; for each line-pair, the epipolar line of the intersection point is used for reducing the search space from two-dimensional to onedimensional This improves the reliability and the computational efficiency of single line matching and provides a reliable initial value for subsequent matching. We demonstrate that combining the virtual intersection of line-pairs and the improved LBD descriptor can effectively improve the quality and the performance of the straight line matching
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