Belief Function Theory - Information Fusion in Signal and Image Processing - page 121

[MIL 01] M ILISAVLJEVI C N., B LOCH I., “A Two-Level Approach for Modeling and Fusion
of Humanitarian Mine Detection Sensors within the Belief Function Framework”, Applied
Stochastic Models and Data Analysis , vol. 2, Compiègne, p. 743-748, 2001.
[NEA 92] N EAPOLITAN R.E., “A Survey of Uncertain and Approximate Inference”, in L.
Z ADEH and J. K APRZYK (ed.) Fuzzy Logic for the Management of Uncertainty , p. 55-82,
J. Wiley, New York, 1992.
[PIN 95] P INZ A., P RANTL M., “Active Fusion for Remote Sensing Image Understanding”,
European Symposium on Satellite Remote Sensing, Paris , vol. 2579, EOS/SPIE, p. 67-77,
1995.
[QUI 89] Q UINIO P., Representation and Accumulation of Uncertain Informations: A Theo-
retical Comparison of Probabilistic and some Non-Probabilistic Formalisms, Report, Ito

Lab., Tohoku University, 1989.
[QUI 91] Q UINIO P. , M ATSUYAMA T., “Random Closed Sets: A Unified Approach to the
Representation of Imprecision and Uncertainty”, in R. K RUSE and P. S IEGEL (ed.) Sym-
bolic and Quantitative Approaches to Uncertainty, ECSQARU , Marseille, Springer Verlag,
p. 282-286, 1991.
[RAS 90] R ASOULIAN H., T HOMPSON W.E., K AZDA L.F., P ARRA -L OERA R., “Application
of the Mathematical Theory of Evidence to the Image Cueing and Image Segmentation
Problem”, SPIE Signal and Image Processing Systems Performance Evaluation , vol. 1310,
p. 199-206, 1990.
[ROM 99] R OMBAUT M., “Fusion de données images segmentées à l'aide du formalisme de
Dempster-Shafer”, GRETSI'99 , Vannes, France, p. 655-658, 1999.
[SCH 93] S CHUBERT J., “On Nonspecific Evidence”, International Journal of Intelligent Sys-
tems , vol. 8, p. 711-725, 1993.
[SHA 76] S HAFER G., A Mathematical Theory of Evidence , Princeton University Press, 1976.
[SME 90a] S METS P., “The Combination of Evidence in the Transferable Belief Model”, IEEE
Transactions on Pattern Analysis and Machine Intelligence , vol. 12, no. 5, p. 447-458, 1990.
[SME 90b] S METS P., “Constructing the Pignistic Probability Function in a Context of Uncer-
tainty”, Uncertainty in Artificial Intelligence , vol. 5, p. 29-39, 1990.
[SME 93] S METS P., “Belief Functions: The Disjunctive Rule of Combination and the Gener-
alized Bayesian Theorem”, International Journal of Approximate Reasoning , vol. 9, p. 1-35,
1993.
[TUP 99] T UPIN F., B LOCH I., M AÎTRE H., “A First Step Towards Automatic Interpretation
of SAR Images using Evidential Fusion of Several Structure Detectors”, IEEE Transactions
on Geoscience and Remote Sensing , vol. 37, no. 3, p. 1327-1343, 1999.
[YAG 87] Y AGER R.R., “On the Dempster-Shafer Framework and New Combination Rules”,
Information Science , vol. 41, p. 93-137, 1987.
[ZAH 92] Z AHZAH E., Contribution à la représentation des connaissances et à leur utilisation
pour l'interprétation automatique des images satellites, PhD Thesis, Paul Sabatier Univer-
sity, Toulouse, 1992.