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Authors: Oussama Zeglazi 1 ; Mohammed Rziza 1 ; Aouatif Amine 2 and Cédric Demonceaux 3

Affiliations: 1 Faculty of Sciences and Mohammed V University, Morocco ; 2 Ibn Tofail University, Morocco ; 3 Univ. Bourgogne Franche-Comté, France

Keyword(s): Stereo Matching, Superpixel, Vertical Median Filter, Scanline Propagation.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Stereo Vision and Structure from Motion

Abstract: In this paper, we propose a new stereo matching algorithm able to reconstruct efficiently a dense disparity maps from few sparse disparity measurements. The algorithm is initialized by sampling the reference image using the Simple Linear Iterative Clustering (SLIC) superpixel method. Then, a sparse disparity map is generated only for the obtained boundary pixels. The reconstruction of the entire disparity map is obtained through the scanline propagation method. Outliers were effectively removed using an adaptive vertical median filter. Experimental results were conducted on the standard and the new Middleburya datasets show that the proposed method produces high-quality dense disparity results.

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Paper citation in several formats:
Zeglazi, O.; Rziza, M.; Amine, A. and Demonceaux, C. (2018). Efficient Dense Disparity Map Reconstruction using Sparse Measurements. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 534-540. DOI: 10.5220/0006557405340540

@conference{visapp18,
author={Oussama Zeglazi. and Mohammed Rziza. and Aouatif Amine. and Cédric Demonceaux.},
title={Efficient Dense Disparity Map Reconstruction using Sparse Measurements},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={534-540},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006557405340540},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Efficient Dense Disparity Map Reconstruction using Sparse Measurements
SN - 978-989-758-290-5
IS - 2184-4321
AU - Zeglazi, O.
AU - Rziza, M.
AU - Amine, A.
AU - Demonceaux, C.
PY - 2018
SP - 534
EP - 540
DO - 10.5220/0006557405340540
PB - SciTePress

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