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8 March 2018 Weighted least square method for epipolar rectification in semi-calibrated image
Guojia Zhu, Huabing Zhou, Yiwei Tao III
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091F (2018) https://doi.org/10.1117/12.2286603
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
The traditional method for dealing with the problem of epipolar rectification in the semi-calibrated case is to use RANdom SAmple Consensus(RANSAC), which could not get a correct parameter when exist serious mismatch points. So the weighted least square method is proposed to solve this problem. First, extracting Scale Invariant Feature Transform(SIFT) and conducting initial feature matching for image pairs. Next, according to the internal geometric relations of corresponding points, transforming the problem into a maximum likelihood estimate problem. And then, each pair of corresponding points is given weight, and the weight is regarded as a latent variable to stand for the precision of correct matching. Finally, weighted least square method and Expectation Maximization(EM) algorithm are used to estimate the latent variable and uncalibrated parameters. Experimental results show that propo- sed method could not only keep rectified precision high, but also has slighter image morphing and faster rectified velocity than state-of-the-art algorithms.
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Guojia Zhu, Huabing Zhou, and Yiwei Tao III "Weighted least square method for epipolar rectification in semi-calibrated image", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091F (8 March 2018); https://doi.org/10.1117/12.2286603
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KEYWORDS
Expectation maximization algorithms
Content addressable memory
Cameras
Unmanned aerial vehicles
Imaging systems
Head
Image morphing
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