Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2153
Title: Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix
Authors: Chahine, Chaza
Vachier-Lagorre, Corinne
Chenoune, Yasmina
Berbari, Racha El 
Fawal, Ziad El
Petit, Eric
Affiliations: Department of Telecommunications and Networking Engineering 
Keywords: Hessian matrices
Unsupervised learning
Subjects: Image segmentation
Issue Date: 2017
Part of: IET image processing journal
Volume: 12
Issue: 4
Start page: 525
End page: 531
Abstract: 
This study deals with information fusion for image segmentation. The evidence theory (or the Dempster-Shafer theory) allows the modellisation of uncertainty and imprecision in the information as well as the combination of different sources. Here, this approach is used in an unsupervised framework to combine the stochastic watershed segmentation which provides several segmentation results, with a Hessian operator in order to obtain a unique and efficient segmentation. The method is tested on natural images from the Berkeley dataset and evaluated using several evaluation metrics. The fusion results surpass those obtained with stochastic watershed alone.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2153
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Telecommunications and Networking Engineering

Show full item record

Record view(s)

5
checked on Jun 22, 2021

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.