SciTePress - Affiliation Profile

Research.Publish.Connect.

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Select a Subject
Refine Ontology Subject

ImViA EA 7535, ERL VIBOT CNRS 6000, Université de Bourgogne Franche Comté (UBFC), 12 Rue de la Fonderie, 71200, Le Creusot and France

Context (please use complete words only)

Field

Sort by

1 Papers

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

Topics: Segmentation and Grouping; Features Extraction; Categorization and Scene Understanding; Vision for Robotics; Deep Learning for Visual Understanding

Authors: Marc Blanchon ¹ , Olivier Morel ¹ , Yifei Zhang ¹ , Ralph Seulin ¹ , Nathan Crombez ² and Désiré Sidibé ¹

Affiliations: ¹ ImViA EA 7535, ERL VIBOT CNRS 6000, Université de Bourgogne Franche Comté (UBFC), 12 Rue de la Fonderie, 71200, Le Creusot and France , ² EPAN Research Group, University of Technology of Belfort-Montbliard (UTBM), 90010, Belfort and France

Digital Object Identifier: 10.5220/0007360203280335

Proceedings of VISAPP 2019/VISIGRAPP, ISBN: 978-989-758-354-4

Quick 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.

Related Ontology Subjects/Areas/Topics (8)

Applications	Image and Video Analysis	Segmentation and Grouping
Computer Vision, Visualization and Computer Graphics	Pattern Recognition	Software Engineering
Features Extraction	Robotics