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|Title:||Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix||Authors:||Chahine, Chaza
Berbari, Racha El
Fawal, Ziad El
|Affiliations:||Department of Telecommunications and Networking Engineering||Keywords:||Hessian matrices
|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|
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