Title :
Unsupervised multisensor data fusion approach
Author :
Salzenstein, Fabien ; Boudraa, Abdel-Ouahab
Author_Institution :
Lab. PHASE, Univ. Louis Pasteur, Strasbourg, France
Abstract :
A new iterative approach of multisensor data fusion based on the Dempster-Shafer (1976) formalism is presented. Mass functions, formalized by a Gaussian model, are estimated at each iteration using the output fused image and the source images. The effectiveness of the method is demonstrated on synthetic images
Keywords :
Gaussian processes; image processing; iterative methods; sensor fusion; signal classification; uncertainty handling; unsupervised learning; Dempster-Shafer theory; Gaussian model; iterative approach; mass functions; output fused image; source images; synthetic images; uncertainty; unsupervised multisensor classification; unsupervised multisensor data fusion; Acoustic sensors; Biomedical imaging; Humans; Iterative methods; Medical robotics; Robot sensing systems; Robotics and automation; Signal processing; Surveillance; Uncertainty;
Conference_Titel :
Signal Processing and its Applications, Sixth International, Symposium on. 2001
Conference_Location :
Kuala Lumpur
Print_ISBN :
0-7803-6703-0
DOI :
10.1109/ISSPA.2001.949798
