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Soft decision optimization method for robust fundamental matrix estimation

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Abstract

It is easy to show that in computer vision, there is the closely coupled relation between feature matching and fundamental matrix estimation. The widely used robust methods such as RANSAC and its improved versions separately deal with feature matching and fundamental matrix estimation. Although these methods are simple to implement, their performance may be relatively low in the presence of gross outliers. By exploiting such coupled relation, the soft decision optimization method is proposed in this paper to estimate the fundamental matrix and find the inlier correspondence set together. Combing feature matching and fundamental matrix estimation, a soft decision objective function is developed to automatically remove the interference of the outliers in the candidate correspondence set. Moreover, an efficient expectation–maximization algorithm is established to find the solution to the fundamental matrix and the inlier correspondence set. Experiments on both synthesized data and real images show that the proposed method can cope with large noise and high ratio of outliers and is superior to some state-of-the-art robust methods in precision, recall, and residual error.

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Acknowledgement

This work is supported by National Natural Science Foundation of China (61271293).

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Authors and Affiliations

  1. Information Engineering College, Henan University of Science and Technology, Luoyang, 471023, Henan, People’s Republic of China

    Chun-Bao Xiao

  2. School of Computer Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Chun-Bao Xiao

  3. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, Shaanxi, People’s Republic of China

    Da-Zheng Feng & Ming-Dong Yuan

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  1. Chun-Bao Xiao
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  2. Da-Zheng Feng
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  3. Ming-Dong Yuan
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Correspondence to Da-Zheng Feng.

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Xiao, CB., Feng, DZ. & Yuan, MD. Soft decision optimization method for robust fundamental matrix estimation. Machine Vision and Applications 30, 657–669 (2019). https://doi.org/10.1007/s00138-019-01019-7

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