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ACCURATE DENSE STEREO MATCHING FOR ROAD SCENES

Oussama Zeglazi 1, * Mohammed Rziza 1 Aouatif Amine 1 Cédric Demonceaux 2
* Corresponding author
1 LRIT
LRIT - Laboratoire de Recherche Informatique et Télécommunications
Abstract : Stereo matching task is the core of applications linked to the intelligent vehicles. In this paper, we present a new variant function of the Census Transform (CT) which is more robust against radiometric changes in real road scenes. We demonstrate that the proposed cost function outperforms the conventional cost functions using the KITTI benchmark. The cost aggregation method is also updated for taking into account the edge information. This enables to improve significantly the aggregated costs especially within homogenous regions. The Winner-Takes-All (WTA) strategy is used to compute disparity values. To further eliminate the remainder matching ambiguities , a post-processing step is performed. Experiments were conducted on the new Middlebury 2 dataset, as well as on the real road traffic scenes of the KITTI database. Obtained disparity results have demonstrated that the proposed method is promising.
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https://hal.archives-ouvertes.fr/hal-01592090
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Submitted on : Friday, September 22, 2017 - 3:40:36 PM
Last modification on : Monday, March 30, 2020 - 8:44:47 AM
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Oussama Zeglazi, Mohammed Rziza, Aouatif Amine, Cédric Demonceaux. ACCURATE DENSE STEREO MATCHING FOR ROAD SCENES. IEEE International Conference on Image Processing, Sep 2017, Beijing, China. ⟨hal-01592090⟩

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