Abstract
Feature matching is essential in computer vision. In this paper, we propose a robust and reliable image feature matching algorithm. It constructs several matching trees in which nodes correspond to traditional sparsely or densely sampled feature points, and feature lines are constructed between the nodes to build a cross-references based on a Difference-of-Gaussians down-sampling pyramid. This can make patch-based descriptors combine efficiently with spatial distributions. By comparing with SIFT, SURF and ORB, our method can get much more correct correspondences on both synthetic and real data under the influence of complex environments or transformations especially in irregular deformation and repeated patterns.
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Sun, X., Ma, X., Chen, L., Wan, L., Zeng, X. (2015). A Tree-Structured Feature Matching Algorithm. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48570-5_19
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DOI: https://doi.org/10.1007/978-3-662-48570-5_19
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