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Communication dans un congrès

Outdoor Scenes Pixel-Wise Semantic Segmentation using Polarimetry and Fully Convolutional Network

Abstract : In this paper, we propose a novel method for pixel-wise scene segmentation application using polarimetry. To address the difficulty of detecting highly reflective areas such as water and windows, we use the angle and degree of polarization of these areas, obtained by processing images from a polarimetric camera. A deep learning framework, based on encoder-decoder architecture, is used for the segmentation of regions of interest. Different methods of augmentation have been developed to obtain a sufficient amount of data, while preserving the physical properties of the polarimetric images. Moreover, we introduce a new dataset comprising both RGB and polarimetric images with manual ground truth annotations for seven different classes. Experimental results on this dataset, show that deep learning can benefit from polarimetry and obtain better segmentation results compared to RGB modality. In particular, we obtain an improvement of 38.35% and 22.92% in the accuracy for segmenting windows and cars respectively.
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Communication dans un congrès
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https://hal-univ-bourgogne.archives-ouvertes.fr/hal-02024107
Contributeur : Désiré Sidibé <>
Soumis le : lundi 18 février 2019 - 21:41:18
Dernière modification le : mardi 3 mars 2020 - 15:20:58
Archivage à long terme le : : dimanche 19 mai 2019 - 20:40:42

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VISAPP_2019_80_CR.pdf
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Marc Blanchon, Olivier Morel, Yifei Zhang, Ralph Seulin, Nathan Crombez, et al.. Outdoor Scenes Pixel-Wise Semantic Segmentation using Polarimetry and Fully Convolutional Network. 14th International Conference on Computer Vision Theory and Applications (VISAPP 2019), Feb 2019, Prague, Czech Republic. ⟨10.5220/0007360203280335⟩. ⟨hal-02024107⟩

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