Intensity guided cost metric for fast stereo matching under radiometric variations

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Reliable and efficient stereo matching is a challenging task due to the presence of multiple radiometric variations. In stereo matching, correspondence between left and right images can become hard owing to low correlation between radiometric changes in left and right images. Previously presented cost metrics are not robust enough against intensive radiometric variations and/or are computationally expensive. In this work, we propose a new similarity metric coined as Intensity Guided Cost Metric (IGCM). IGCM turns out to significantly contribute to the depth accuracy by rejecting outliers and reducing the edge-fattening effect in object boundaries. IGCM is further combined explicitly with a color formation model to handle various radiometric changes that occur between stereo images. Experimental results on Middlebury dataset show 13.8%, 22.8%, 20.9%, 19.5 % and 9.1% decrease in average error rate compared to Adaptive Normalized Cross-Correlation (ANCC), Dense Adaptive Self-Correlation (DASC), Adaptive Descriptor(AD), Fast Cost Volume Filtering (FCVF) and Iterative Guided Filter (IGF)-based methods, respectively. Moreover, using integral images IGCM can achieve a speedup of 20x, 6x, 41x, 25x and 45x compared to the aforementioned methods. (c) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Publisher
OPTICAL SOC AMER
Issue Date
2018-02
Language
English
Article Type
Article
Citation

OPTICS EXPRESS, v.26, no.4, pp.4096 - 4111

ISSN
1094-4087
DOI
10.1364/OE.26.004096
URI
http://hdl.handle.net/10203/240948
Appears in Collection
EE-Journal Papers(저널논문)
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