Evidential split-and-merge: Application to object-based image analysis - Ifsttar Accéder directement au contenu
Article Dans Une Revue International Journal of Approximate Reasoning Année : 2018

Evidential split-and-merge: Application to object-based image analysis

Résumé

This paper addresses the difficult problem of segmenting objects in a scene and simultaneously estimating their material class. Focusing on the case where, individually, no dataset can achieve such a task, multiple sensor datasets are considered, including some images for retrieving the spatial information. The proposed approach is based on mutual validation between class decision (using the most relevant dataset) and segmen-tation (derived from image data). The main originality relies in the ability to make these two modules (classification and segmentation) interactive. Specifically, our application focuses on object-level material labeling using classic RGB images, laser profilome-ter images and a NIR spectral sensor. Starting from a superpixel segmentation, the relevant data are introduced as constraints modifying the initial segmentation in a split-and-merge process, which interacts with the material labeling process. In this work, we use the belief function framework to model the information extracted from each kind of data and to transfer it from one processing module to another. In particular we show the relevance of evidential conflict measure to drive the split process and to control the merge one. Experiments have been performed on actual scenes with stacked objects and difficult cases of material such as transparent polymers. They allow us to assess the performance of the proposed approach both in terms of material labeling and object segmentation as well as to illustrate some borderline cases.
Fichier principal
Vignette du fichier
LachaizeIJAR18.pdf (19.31 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01918408 , version 1 (10-11-2018)

Identifiants

Citer

Marie Lachaize, Sylvie Le Hégarat-Mascle, Emanuel Aldea, Aude Maitrot, Roger Reynaud. Evidential split-and-merge: Application to object-based image analysis. International Journal of Approximate Reasoning, 2018, 103, pp.303-319. ⟨10.1016/j.ijar.2018.10.008⟩. ⟨hal-01918408⟩
66 Consultations
79 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More