Definitions - Information Fusion in Signal and Image Processing - page 11

The major differences with other application fields of information fusion first stem
from the fact that the essential question (and therefore the objective of fusion) is not the
same. In signal and image processing, it consists essentially, according to Definition
1.1, of improving our knowledge of the world (as it is). This implies the existence of
a truth, even if we only have access to a partial or deformed version of it, or if it is
difficult to obtain, as opposed to the fusion of preferences (the way we want the world
to be), the fusion of regulations (the way the world should be), or voting problems,
where typically there is no truth, etc. [BLO 01].

1.7. Bibliography

[BAR 88] B AR -S HALOM Y. , F ORTMANN T.E., Tracking and Data Association , Academic

Press, San Diego, California, 1988.

[BLO 94] B LOCH I., M AÎTRE H., “Fusion de données en traitement d'images: modèles
d'information et décisions”, Traitement du Signal , vol. 11, no. 6, p. 435-446, 1994.
[BLO 96] B LOCH I., “Incertitude, imprécision et additivité en fusion de données: point de vue
historique”, Traitement du Signal , vol. 13, no. 4, p. 267-288, 1996.

[BLO 01] B LOCH I., H UNTER A. ( ED .), “Fusion: General Concepts and Characteristics”,
International Journal of Intelligent Systems , vol. 16, no. 10, p. 1107-1134, October 2001.

[COX 46] C OX R.T., “Probability, Frequency and Reasonable Expectation”, Journal of Phys-
ics , vol. 14, no. 1, p. 115-137, 1946.

[DAS 96] D ASARATHY B.V., “Fusion Strategies for Enhancing Decision Reliability in Multi-
Sensor Environments”, Optical Engineering , vol. 35, no. 3, p. 603-616, March 1996.

[DUB 88] D UBOIS D., P RADE H., Possibility Theory , Plenum Press, New York, 1988.

[HAL 97] H ALL D.L., L LINAS J., “An Introduction to Multisensor Data Fusion”, Proceed-
ings of the IEEE , vol. 85, no. 1, p. 6-23, 1997.
[JDL 91] Data Fusion Lexicon, Data Fusion Subpanel of the Joint Directors of Laboratories
Technical Panel for C 3 , F. E. White, Code 4202, NOSC, San Diego, California, 1991.

[KLI 88] K LIR G.J., F OLGER T.A., Fuzzy Sets, Uncertainty, and Information , Prentice Hall,
Englewood Cliffs, 1988.

[LEU 96] L EUNG H., “Neural Networks Data Association with Application to Multiple-Target
Tracking”, Optical Engineering , vol. 35, no. 3, p. 693-700, March 1996.

[ROM 96] R OMINE J.B., K AMEN E.W., “Modeling and Fusion of Radar and Imaging Sensor
Data for Target Tracking”, Optical Engineering , vol. 35, no. 3, p. 659-673, March 1996.

[THO 90] T HOMOPOULOS S.C.A., “Sensor Integration and Data Fusion”, Journal of Robot-
ics Systems , vol. 7, no. 3, p. 337-372, 1990.

[WAL 99] W ALD L., “Some Terms of Reference in Data Fusion”, IEEE Transactions on

Geoscience and Remote Sensing , vol. 37, no. 3, p. 1190-1193, 1999.