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Image registration via low-rank factorization and maximum rank resolving

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Abstract

The feature-based image registration method has a better performance in terms of robustness to the intensity variance, but its accuracy of the feature-based image registration still could be improved. This paper utilizes the low-rank factorization and maximum rank resolving to improve the accuracy of image registration. In detail, the proposed method extracts coarse geometrical transform parameters based on the feature point pairs between images, then constructs low-rank model to optimize the geometrical transform parameters and estimate the inliers. Finally, an iterative optimization strategy is introduced to acquire the optimized transform parameters by maximum rank resolving. Experimental results illustrate that the proposed approach presents a good performance in terms with the root residual mean squares error and the entropy of image difference.

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Acknowledgments

The authors thank the anonymous reviewers for helping to review this paper. This work was supported by China 973 Program Grant (no.2013CB328903), China 2011 Internet of Things development of Ministry of Industry and Information Technology (2011BAJ03B13-2) and Chongqing Key Project of Science and Technology of China (cstc2012gg-yyjs40008).

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Authors and Affiliations

  1. School of Automation, Chongqing University, Chongqing, 400044, China

    Wenjie Zhang

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University, Chongqing, 400044, China

    Wenjie Zhang & Qingyu Xiong

  3. School of Software Engineering, Chongqing University, Chongqing, 400044, China

    Qingyu Xiong

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  1. Wenjie Zhang
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Correspondence to Qingyu Xiong.

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Zhang, W., Xiong, Q. Image registration via low-rank factorization and maximum rank resolving. Multimed Tools Appl 76, 23643–23659 (2017). https://doi.org/10.1007/s11042-016-4125-4

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