PhD opportunities

Segmentation and classification of road scenes by analysis of LIDAR data, color and multi-spectral images

Area of expertise Real-time computer science, robotics,systems and control - Paris
Doctoral School SMI - Sciences des Mtiers de l'Ingnieur
Title Segmentation and classification of road scenes by analysis of LIDAR data, color and multi-spectral images
Supervisor Mme Beatriz MARCOTEGUI
Co-supervisor M. Franois GOULETTE
Contact
Research unit Mathematics and Systems
CMM - Centre de Morphologie Mathmatique
Keywords lidar, classification, mobile mapping, materials, multi-spectral
Abstract The objective of this thesis is to integrate multi-spectral data in order to improve the robustness of the semantization methods. Indeed, this information will allow a better discrimination of certain materials and thus help to remove ambiguities, for example between the vegetation class and other similar structures such as bus shelters, faades or road signs. It will thus be possible not only to identify the different materials, but also to determine their concentrations, adding a quantitative dimension to the description of the scene. It will also be possible to provide additional information needed for simulation purposes. For example, most analysis methods only distinguish the ground from the rest of the scene, whereas realistic rendering requires a different material to be assigned to the ground marking. MINES ParisTech has been studying hyper-spectral data for about ten years and has the required expertise to address this topic [VELASCO13]. The proposed thesis aims to push the scientific limits of these problems and to develop concrete applications.
This work will be part of the FUI REPLICA project which objective is to automatically produce 3D databases and a photo-realistic simulation capability of environments for autonomous vehicle validation.
Funding Financement sur programme europen ou multilatral
Partnership
Starting date October 1st 2017
Date of first publication June 7th 2017