Abstract
This paper proposes a new similarity measure that is invariant to global and local affine illumination changes. Unlike existing methods, its computational complexity is very low. When used for stereo correspondence estimation, its computational complexity is linear in the number of image pixels and disparity searching range. It also outperforms the current state of the art similarity measures in terms of accuracy on the Middlebury benchmark (with radiometric differences).
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The trained patch size for Census transform is \(19 \times 19\).
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Acknowledgments
This work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 21201914).
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Communicated by Masatoshi Okutomi.
Appendix
Appendix
1.1 Appendix 1: Derivation of Eq. 12
The matching cost measured from two corresponding pixels p and \(p'\) in two grayscale images \(I_L\) and \(I_R\) is:
1.2 Appendix 2: Derivation of Eq. 22
Similar to Eq. 4, we can extend Eq. 20 for color images as follows:
\(\tilde{\mathcal {X}}\) is defined in Eq. 21, and
and
The linear system presented in Eq. 28 can be rewritten as:
where
and
1.3 Appendix 3: Derivation of Eq. 23
The matching cost for color images is:
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Xu, J., Yang, Q., Tang, J. et al. Linear Time Illumination Invariant Stereo Matching. Int J Comput Vis 119, 179–193 (2016). https://doi.org/10.1007/s11263-016-0886-5
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DOI: https://doi.org/10.1007/s11263-016-0886-5