Information fusion for unsupervised image segmentation using stochastic watershed and Hessian matrix
Résumé
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.