Skip to Main content Skip to Navigation

Estimation de profondeur à partir d'images monoculaires par apprentissage profond

Michel Moukari 1
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Computer vision is a branch of artificial intelligence whose purpose is to enable a machine to analyze, process and understand the content of digital images. Scene understanding in particular is a major issue in computer vision. It goes through a semantic and structural characterization of the image, on one hand to describe its content and, on the other hand, to understand its geometry. However, while the real space is three-dimensional, the image representing it is two-dimensional. Part of the 3D information is thus lost during the process of image formation and it is therefore non trivial to describe the geometry of a scene from 2D images of it.There are several ways to retrieve the depth information lost in the image. In this thesis we are interested in estimating a depth map given a single image of the scene. In this case, the depth information corresponds, for each pixel, to the distance between the camera and the object represented in this pixel. The automatic estimation of a distance map of the scene from an image is indeed a critical algorithmic brick in a very large number of domains, in particular that of autonomous vehicles (obstacle detection, navigation aids).Although the problem of estimating depth from a single image is a difficult and inherently ill-posed problem, we know that humans can appreciate distances with one eye. This capacity is not innate but acquired and made possible mostly thanks to the identification of indices reflecting the prior knowledge of the surrounding objects. Moreover, we know that learning algorithms can extract these clues directly from images. We are particularly interested in statistical learning methods based on deep neural networks that have recently led to major breakthroughs in many fields and we are studying the case of the monocular depth estimation.
Document type :
Complete list of metadatas

Cited literature [146 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Thursday, January 2, 2020 - 1:51:07 AM
Last modification on : Monday, February 10, 2020 - 3:48:30 PM


Version validated by the jury (STAR)


  • HAL Id : tel-02426260, version 1


Michel Moukari. Estimation de profondeur à partir d'images monoculaires par apprentissage profond. Traitement des images [eess.IV]. Normandie Université, 2019. Français. ⟨NNT : 2019NORMC211⟩. ⟨tel-02426260⟩



Record views


Files downloads