050-Uncertain and Incomplete knowledge analysis in 3D surface model processing
050-Uncertain and Incomplete knowledge analysis in 3D surface model processing
- Contract :Ph.D.
- Duration :36 months
- Working time :Full-time
- Experience :Entry Level
- Education level :Master’s Degree, MA/MS/MSc
Your mission at CNES :
CNES has a 3D restitution pipeline which generates, from an image pair, the corresponding Digital Elevation Model (DEM). This 3D tool called CARS is publicly available.This pipeline is composed of the following classical 3D steps:
Epipolar geometry transformation (image alignment)
Dense matching between images to identify homologous points: Pandora tool
Triangulation of homologous points thanks to the image geometric models
Rasterization from a point cloud to a 2.5D image
- on the input data (geometric models, noise on the image detectors, etc.)
- on the method for computing epipolar images
- on dense matching method
- on rasterization method
While probability distributions are sufficient to model purely stochastic or random uncertainty, many works suggest that it is not sufficient to describe uncertainty due to lack of knowledge or imprecision. This modeling can be done, for example, by using sets of probabilities or fuzzy sets.
This thesis aims at analyzing the different types of uncertainty and to see how to represent them using a wide set of mathematical tools like belief functions, distributions of possibilities, etc. The objective is to apply mathematical tools for modelling and propagate imprecise and uncertain knowledge through the aforementioned pipeline, and to check how classical approaches currently used can be improved to better take into account and better quantify uncertainty in the final 3D result.
Identify mathematical methods for representing and propagating uncertainties in the 3D pipeline
Define how to represent information of a random or imprecise nature in the pipeline
Understand how uncertainty propagate within the pipeline
Quantify the impact of uncertainty in the different stages in order to be able to identify which parameters impact the most on the final uncertainty.
Select which uncertainties to primarily reduce according to their characteristics and importance in order to improve the confidence in 3D restitution
Generating an uncertainty interval for the elevation measurement and a way to represent uncertainty in the end results to users.
Providing avenues for understanding uncertainty in the 3D pipeline
Candidate profile searched:
Msc degree or equivalent, with strong skills in mathematics and/or computer science, ideally with a specialty in image processing. Will to engage into uncertainty treatment problems and into multi-disciplinary research.
Good english skills (written and oral) requested, excellent skills desired.
We suggest you to contact first the PhD supervisor about the topics and the co-financial partner (found by the lab !). Then, prepare a resumé, a recent transcript and a reference letter from you M2 supervisor/ engineering school director and you will be ready to apply online !
CNES will inform about the status of your application in mid-June.
More details on CNES website : https://cnes.fr/en/web/CNES-en/10685-st-doctoral-grants.php
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Our address
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