Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

Pattern Recognition Letters

Volumn 34, Issue 14, 2013, Pages 1719-1724

  Maravall, Darío ^a   De Lope, Javier ^a   Fuentes, Juan Pablo ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Landmarks recognition; Learning automata theory; Supervised classification; Unmanned aerial vehicles; Visual based navigation

Indexed keywords

AIR NAVIGATION; AUTOMATA THEORY; E-LEARNING; IMAGE CLASSIFICATION; KNOWLEDGE BASED SYSTEMS; UNMANNED AERIAL VEHICLES (UAV);

CLASSIFICATION ACCURACY; LANDMARKS RECOGNITION; LEARNING AUTOMATA THEORIES; PATTERN RECOGNITION METHOD; PROBABILISTIC CLASSIFICATION; PROBABILISTIC KNOWLEDGE; SELF-LEARNING CAPABILITY; SUPERVISED CLASSIFICATION;

PATTERN RECOGNITION;

EID: 84885641194     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2013.03.019     Document Type: Article

References (14)

1. Bargiela, A., Pedrycz, W. Granular Computing. An Introduction. Kluwer Academic Publishers., Boston.
2. Michie, D., Chambers, R.A. Boxes: an experiment in adaptive control. In: Dale, E., Michie, D. (Eds.), Machine Intelligence 2. Oliver & Boyd, pp. 137-152.
3. Narendra, K., Thathachar, M. Learning automata-A survey. IEEE Transactions Systems Man and Cybernetics 4 (4), 323-334.
4. Narendra, K., Wright, E., Mason, L. Applications of learning automata to telephone traffic routing and control. IEEE Transactions on Systems Man and Cybernetics 7 (11), 785-792.
5. Narendra, K., Viswanathan, R. A two-level system of stochastic automata for periodic random environments. IEEE Transactions on Systems Man and Cybernetics 2 (2), 285-289.
6. Maravall, D. New reinforcement algorithm in learning automata. Electronic Letters 16 (16), 623-624.
7. D. Maravall, New Solutions in Adaptive Signal Detection, First Prize IEEE Region 8 PSPC, Eurocon 80, Stuttgart, 1980.
8. S. Piskorski, N. Brulezi, F. D'Haeyer, AR.Drone Developer Guide, SDK 2.0, Parrot, 2012.
9. Martin, J.A., de Lope, J. Learning autonomous helicopter flight with evolutionary reinforcement learning. In: Computer Aided Systems Theory. In: Moreno-Diaz, R., Pichler, F., Quesada, A. (Eds.), . LNCS, vol. 5717. Springer Verlag, pp. 75-82.
10. de Lope, J., San Martin, J.J., Martin, J.A., Helicopter flight dynamics using softcomputing models. In: Computer Aided Systems Theory. In: Moreno-Diaz, R., Pichler, F., Quesada, A. (Eds.), . LNCS, vol. 4739. Springer Verlag, pp. 621-628.
11. Barlow, J. The Cerebellum and Adaptive Control. Cambridge University Press.
12. Imamizu, H., Miyauchi, S., Tamada, T., Sasaki, Y., Takino, R., Pütz, B., Yoshioka, T., Kawato, M. Human cerebellar activity reflecting an acquired internal model of a new tool. Nature 403, 192-195.
13. Xu, G., Zhang, Y., Ji, S., Cheng, Y., Tian, Y. Research on computer vision-based for UAV autonomous landing on a ship. Pattern Recognition Letters 30, 600-605.
14. Rizzi, A., Duina, D., Inelli, S., Cassinis, R. A novel visual landmark matching for a biologically inspired homing. Pattern Recognition Letters 22, 1371-1378.