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ICARCV 2018, 15th International Conference on Control, Automation, Robotics and Vision 10/11/2018 21/11/2018 Singapour Singapour Proceedings IEEE
nom du congrès :
ICARCV 2018, 15th International Conference on Control, Automation, Robotics and Vision
début du congrès :
10/11/2018
fin du congrès :
21/11/2018
ville du congrès :
Singapour
pays du congrès :
Singapour
site des actes du congrès :
|
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A stixel approach for enhancing semantic image segmentation using prior map information / Sylvain Jonchery (2018)
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Titre : A stixel approach for enhancing semantic image segmentation using prior map information Type de document : Article/Communication Auteurs : Sylvain Jonchery, Auteur ; Guillaume Bresson, Auteur ; Bruno Vallet , Auteur ; Rafal Żbikowski, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : ICARCV 2018, 15th International Conference on Control, Automation, Robotics and Vision 10/11/2018 21/11/2018 Singapour Singapour Proceedings IEEE Importance : pp 1715 - 1720 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] précision de la classification
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) A key problem for autonomous car navigation is the understanding, at an object level, of the current driving situation. Addressing this issue requires the extraction of meaningful information from on-board stereo imagery by classifying the fundamental elements of urban scenes into semantic categories that can more easily be interpreted and be reflected upon (streets, buildings, pedestrians, vehicles, signs, etc.). A probabilistic method is proposed to fuse a coarse prior 3D map data with stereo imagery classification. A novel fusion architecture based on the Stixel framework is presented for combining semantic pixel-wise segmentation from a convolutional neural network (CNN) with depth information obtained from stereo imagery while integrating coarse prior depth and label information. The proposed approach was tested on a manually labeled data set in urban environments. The results show that the classification accuracy of the fundamental elements composing the urban scene was significantly enhanced by this method compared to what is obtained from the semantic pixel-wise segmentation of a CNN alone. Numéro de notice : C2018-094 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICARCV.2018.8581150 Date de publication en ligne : 20/12/2018 En ligne : https://doi.org/10.1109/ICARCV.2018.8581150 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94247