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Conference papers

Polarimetric image augmentation

Abstract : This paper deals with new augmentation methods for an unconventional imaging modality sensitive to the physics of the observed scene called polarimetry. In nature, polarized light is obtained by reflection or scattering. Robotics applications in urban environments are subject to many obstacles that can be specular and therefore provide polarized light. These areas are prone to segmentation errors using standard modalities but could be solved using information carried by the polarized light. Deep Convolutional Neural Networks (DCNNs) have shown excellent segmentation results, but require a significant amount of data to achieve best performances. The lack of data is usually overcomed by using augmentation methods. However, unlike RGB images, polarization images are not only scalar (intensity) images and standard augmentation techniques cannot be applied straightforwardly. We propose enhancing deep learning models through a regularized augmentation procedure applied to polarimetric data in order to characterize scenes more effectively under challenging conditions. We subsequently observe an average of 18.1% improvement in IoU between not augmented and regularized training procedures on real world data.
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Contributor : Désiré Sidibé Connect in order to contact the contributor
Submitted on : Monday, October 26, 2020 - 9:54:24 AM
Last modification on : Friday, April 15, 2022 - 10:58:04 AM
Long-term archiving on: : Wednesday, January 27, 2021 - 6:26:23 PM


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  • HAL Id : hal-02977822, version 1


Marc Blanchon, Olivier Morel, Fabrice Meriaudeau, Ralph Seulin, Désiré Sidibé. Polarimetric image augmentation. 25th International Conference on Pattern Recognition (ICPR 2020), Jan 2021, Milan, Italy. ⟨hal-02977822⟩



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