Unsupervised Dempster-Shafer fusion of dependent sensors

Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Volumn 2000-January, Issue , 2000, Pages 247-251

  Pieczynski, W ^a  

^a INSTITUT NATIONAL DES TÉLÉCOMMUNICATIONS   (France)

Author keywords

Bayesian methods; Clouds; Hidden Markov models; Ice; Image segmentation; Image sensors; Laser radar; Optical sensors; Parameter estimation; Sensor fusion

Indexed keywords

BAYESIAN NETWORKS; CLOUDS; GAUSSIAN NOISE (ELECTRONIC); HIDDEN MARKOV MODELS; ICE; IMAGE ANALYSIS; IMAGE FUSION; IMAGE SENSORS; MARKOV PROCESSES; OPTICAL RADAR; OPTICAL SENSORS; PARAMETER ESTIMATION; TRELLIS CODES;

BAYESIAN FUSION; BAYESIAN METHODS; DEMPSTER-SHAFER; DEMPSTER-SHAFER FUSION; DEPENDENT SENSORS; PROBABILISTIC MODELS; SENSOR FUSION; UNSUPERVISED SEGMENTATION METHOD;

IMAGE SEGMENTATION;

EID: 84950273611     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IAI.2000.839609     Document Type: Conference Paper

References (16)

1. A. Appriou, Probabilités et incertitude en fusion de données multisenseurs, Revue Scientifique et Technique de la Défense, No. 11, pp. 27-40, 1991.
2. nd IEEE International Conference on Intelligent Processing Systems (ICIPS'98), Gold Coast, Australia, August 4-7, 1998.
3. D. Dubois, S. Moral and H. Prade. Belief change rules in ordinal and numerical uncertainty theories. In D. M. Gabbay and Ph Smets (eds), Handbook of defeasible reasoning and uncertainty management systems, vol. 3, 311-392, Kluwer.
4. A. Chardin and P. Pérez, Semi-Iterative Inference With Hierarchical Energy-Based Models for Image Analysis, International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, York, United Kingdom, July 1999, pp. 83-98, Springer LNCS 1654.
5. T. Denoeu, Analysis of evidence-theoretic decision rule for pattern classification, Pattern Recognition, Vol. 30, No. 7, pp. 1095-1107, 1997.
6. S. Geman, G. Geman, Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Trans. on PAMI, Vol. 6, No. 6, pp. 721-741, 1984.
7. J. Guan and D. A. Bell, Evidence Theory and Its Applications, North-Holland, Amsterdam, 1991.
8. N. Giordana and W. Pieczynski, Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation, IEEE Transactions on PAMI, Vol. 19, No. 5, pp. 465-475, 1997.
9. Z. Kato, J. Zerubia, and M. Berthod, "Unsupervised parallel image classification using Markovian models, Pattern Recognition, Vol. 32, n. 4, pp. 591-604, Avril 1999.
10. J.-M. Laferté, P. Pérez, and F. Heitz, Discrete Markov Image Modeling and Inference on Quadtree, IEEE Transactions on IP, 2000.
11. J. Marroquin, S. Mitter, T. Poggio, Probabilistic solution of ill-posed problems in computational vision, Journal of the American Statistical Association, 82, pp. 76-89, 1987.
12. W. Pieczynski, J. Bouvrais, and C. Michel, Estimation of Generalized Mixture in the Case of Correlated Sensors, IEEE Transactions on IP, Vol. 09, No. 2, February 2000.
13. W. Pieczynski, Hidden Evidential Markov Trees and Image Segmentation, IEEE International Conference on Image Processing, "ICIP'99", 24-28 October 1999, Kobe, Japan.
14. P. Rostaing, J.-N. Provost, and C. Collet, Unsupervised Multispectral Image Segmentation Using Generalized Gaussian Noise Model, International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, York, United Kingdom, July 1999, pp. 142-156, Springer LNCS 1654.
15. G. Shafer, A mathematical theory of evidence, Prinston University Press, Prinston, 1976.
16. L. Younes, Estimation and annealing for Gibbsian fields, Annales de l'Institut Henri Poincaré, Vol. 24, No. 2, 1988, pp. 269-294.