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8 March 2018 Stereo matching algorithm based on double components model
Xiao Zhou, Kejun Ou, Jianxin Zhao, Xingang Mou
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060911 (2018) https://doi.org/10.1117/12.2285260
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
The tiny wires are the great threat to the safety of the UAV flight. Because they have only several pixels isolated far from the background, while most of the existing stereo matching methods require a certain area of the support region to improve the robustness, or assume the depth dependence of the neighboring pixels to meet requirement of global or semi global optimization method. So there will be some false alarms even failures when images contains tiny wires. A new stereo matching algorithm is approved in the paper based on double components model. According to different texture types the input image is decomposed into two independent component images. One contains only sparse wire texture image and another contains all remaining parts. Different matching schemes are adopted for each component image pairs. Experiment proved that the algorithm can effectively calculate the depth image of complex scene of patrol UAV, which can detect tiny wires besides the large size objects. Compared with the current mainstream method it has obvious advantages.
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Xiao Zhou, Kejun Ou, Jianxin Zhao, and Xingang Mou "Stereo matching algorithm based on double components model", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060911 (8 March 2018); https://doi.org/10.1117/12.2285260
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KEYWORDS
Unmanned aerial vehicles
Computer vision technology
Machine vision
Visual process modeling
Image filtering
Image fusion
Signal to noise ratio
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