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Applications of belief functions

  1. C. Lian, S. Ruan, T. Denoeux, H. Li and P. Vera. Joint Tumor Segmentation in PET-CT Images using Co-Clustering and Fusion based on Belief Functions. IEEE Transactions on Image Processing, vol. 28, Issue 2, pages 755-766, 2019. pdf
  2. L. Sui, P. Feissel and T. Denoeux. Identification of Elastic Properties in the Belief Function Framework. International Journal of Approximate Reasoning, vol. 101, pages 69-87, 2018. pdf
  3. C. Lian, S. Ruan, T. Denoeux, H. Li and P. Vera. Spatial Evidential Clustering with Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. IEEE Transactions on Biomedical Engineering, Volume 65, Issue 1, pages 21-30, 2018. pdf
  4. J.-B. Bordes, F. Davoine, Ph. Xu and T. Denoeux. Evidential Grammars: A Compositional Approach For Scene Understanding. Application To Multimodal Street Data. Applied Soft Computing, Volume 61, Pages 1173-1185, 2017. pdf
  5. C. Lian, S. Ruan, T. Denoeux, F. Jardin, P. Vera. Selecting Radiomic Features from FDG-PET Images for Cancer Treatment Outcome Prediction. Medical Image Analysis, Volume 32, Pages 257-268, 2016. pdf
  6. Ph. Xu, F. Davoine, J.-B. Bordes, H. Zhao and Th. Denoeux. Multimodal Information Fusion for Urban Scene Understanding. Machine Vision and Applications, Volume 27, Issue 3, pp 331-349, 2016. pdf
  7. B. Lelandais, S. Ruan, T. Denoeux, P. Vera, I. Gardin. Fusion of multi-tracer PET images for Dose Painting. Medical Image Analysis, Volume 18, Issue 7, Pages 1247-1259, 2014. pdf
  8. Ph. Xu, F. Davoine, J.-B. Bordes, T. Denoeux. Fusion d'informations pour la compréhension de scènes. Traitement du Signal, Vol. 31, Number 1-2, pages 57-80, 2014. pdf
  9. Z. L. Cherfi, L. Oukhellou, E. Côme, T. Denoeux and P. Aknin. Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: Application to railway track circuit diagnosis. Soft Computing, Vol. 16, Number 5, pages 741-754, 2012. pdf
  10. F. Abdallah, G. Nassreddine and T. Denoeux. A multiple-hypotheses map matching method suitable for weighted and box-shaped state estimation for localization. IEEE Transactions on Intelligent Transportation Systems, Vol. 12, Issue 4, pages 1495-1510, 2011. pdf
  11. L. Oukhellou, A. Debiolles, T. Denoeux and P. Aknin. Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion. Engineering Applications of Artificial Intelligence, Vol. 23, pages 117-128, 2010. pdf
  12. K. Worden, G. Manson, T. Denoeux. An evidence-based approach to damage location on an aircraft structure. Mechanical Systems and Signal Processing, Vol. 23, Issue 6, pages 1792-1804, 2009. pdf
  13. D. Mercier, G. Cron, T. Denoeux and M.-H. Masson. Decision fusion for postal address recognition using belief functions. Expert Systems with Applications, Vol. 36, Issue 3, pages 5643-5653, 2009. pdf
  14. D. Mercier, G. Cron, T. Denoeux and M.-H. Masson. Fusion de décisions postales dans le cadre du Modèle des Croyances Transférables. Traitement du Signal, Vol. 24, Issue 2, pages 133-151, 2007. pdf
  15. S. Démotier, W. Schön and T. Denoeux. Risk Assessment Based on Weak Information using Belief Functions: A Case Study in Water Treatment, IEEE Transactions on Systems, Man and Cybernetics C, , Vol. 36, Issue 3, 382-396, 2006. postscript, pdf

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