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Robust perspective invariant quasidense matching across large oblique images

[+] Author Affiliations
Guo-Biao Yao

Shandong Jianzhu University, School of Civil Engineering, No.1000 Fengming Road, Jinan 250101, China

China University of Mining and Technology, School of Environment Science and Spatial Informatics, No. 1 Daxue Road, Xuzhou 221116, China

Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Beijing 100830, China

Ka-Zhong Deng

China University of Mining and Technology, School of Environment Science and Spatial Informatics, No. 1 Daxue Road, Xuzhou 221116, China

Li Zhang

Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Beijing 100830, China

Hai-Bin Ai

Chinese Academy of Surveying and Mapping, No. 28 Lianhuachi West Road, Beijing 100830, China

J. Electron. Imaging. 23(4), 043005 (Jul 10, 2014). doi:10.1117/1.JEI.23.4.043005
History: Received January 11, 2014; Revised May 12, 2014; Accepted June 6, 2014
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Abstract.  Large oblique stereo images are of great interest because they have a large coverage and a high reconstruction precision. However, severe distortions are more likely to occur in this type of image. This can make conventional algorithms inaccurate, computationally expensive, and invalid. We present a new robust quasidense image matching algorithm for large oblique images. Our algorithm can be divided into two steps. First, we find a sufficient number of highly accurate seed matches that are uniformly distributed by integrating complementary affine invariant feature matching with least-squares matching. Second, we consider quasidense matches covering overlapping areas of images. We use match propagation beginning with the best seed, iteratively applying the local perspective invariant neighborhood transform (PINT) with the normalized cross-correlation metric. The local PINT is dynamically updated using the current new match set, and the erroneous matches are eliminated according to their geometric consistency. We conducted experiments on simulated and real large oblique images to demonstrate that the proposed algorithm is effective and can robustly find quasidense matches. Comparisons with the existing methods demonstrated that it is superior in terms of accuracy and efficiency.

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Topics

Algorithms ; Matrices

Citation

Guo-Biao Yao ; Ka-Zhong Deng ; Li Zhang and Hai-Bin Ai
"Robust perspective invariant quasidense matching across large oblique images", J. Electron. Imaging. 23(4), 043005 (Jul 10, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.4.043005


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