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A robust non-iterative method for similarity transform estimation

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Abstract

A non-iterative and robust method—direct outliers remove (DOR) is proposed, which efficiently estimates the similarity transform based on a data set containing both correct and incorrect correspondences. Unlike hypothesize-and-test methods such as Random Sample Consensus algorithm and its variants, DOR removes mismatches by exploring all the correspondences only once, using two invariant features of similarity transform. One is the angles between two vectors and the other is the length ratios of corresponding vectors. Given two images related by similarity transform, experiments demonstrate that all the mismatches introduced in matching stage could be detected and removed. Without losing computational accuracy, DOR is faster compared with several hypothesize-and-test algorithms, especially when the percentage of correct correspondence is relatively low.

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Authors and Affiliations

  1. School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics (Beihang University), 37 Xue Yuan Road, Hai Dian District, Beijing, 100191, People’s Republic of China

    Li Yinan, Yang Lingyu & Shen Gongzhang

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  1. Li Yinan
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  2. Yang Lingyu
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  3. Shen Gongzhang
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Correspondence to Li Yinan.

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Yinan, L., Lingyu, Y. & Gongzhang, S. A robust non-iterative method for similarity transform estimation. Machine Vision and Applications 24, 637–649 (2013). https://doi.org/10.1007/s00138-012-0412-x

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  • DOI: https://doi.org/10.1007/s00138-012-0412-x

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