Active object recognition by view integration and reinforcement learning

Robotics and Autonomous Systems

Volumn 31, Issue 1, 2000, Pages 71-86

  Paletta, Lucas ^a   Pinz, Axel ^a  

^a UNIVERSITY OF GRAZ   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

CAMERAS; MARKOV PROCESSES; MOBILE ROBOTS; PROBABILITY; ROBOT LEARNING; SENSOR DATA FUSION;

ACTIVE OBJECT RECOGNITION; DEGREES OF FREEDOM; INFORMATION FUSION; RADIAL BASIS FUNCTION NETWORK; REINFORCEMENT LEARNING; SENSORIMOTOR MAPPINGS; VIEW INTEGRATION; VIEWPOINT PLANNING;

OBJECT RECOGNITION;

EID: 0033741106     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0921-8890(99)00079-2     Document Type: Article

References (36)

1. Y. Aloimonos, Purposive and qualitative active vision, in: Proceedings of the International Conference on Pattern Recognition, 1990, pp. 346-360.
2. L. Baird, Residual algorithms: Reinforcement learning with function approximation, in: Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA, 1995, pp. 30-37.
3. Ballard D.H., Brown C.H. Principles of animate vision. CVGIP: Image Understanding. 56(1):1992;3-21.
4. C. Bandera, F.J. Vico, J.M. Bravo, M.E. Harmon, L.C. Baird, Residual Q-learning applied to visual attention, in: Proceedings of the 13th International Conference on Machine Learning, Bari, Italy, 1996, pp. 20-27.
5. H. Borotschnig, L. Paletta, M. Prantl, A. Pinz, Active object recognition in parametric eigenspace, in: Proceedings of the Ninth British Machine Vision Conference, Southampton, UK, 1998, pp. 629-638; Extended version submitted to Image and Vision Computing.
6. Bradski G., Grossberg S. Fast learning VIEWNET architectures for recognizing three-dimensional objects from multiple two-dimensional views. Neural Networks. 8(7/8):1995;1053-1089.
7. J.S. Bridle, Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition, in: F. Fogelman Soulié, J. Hérault (Eds.), Neurocomputing: Algorithms, Architectures and Applications, Springer, New York, 1990, pp. 227-236.
8. H.H. Bülthoff, S. Edelman, Psychophysical support for a 2D view interpolation theory of object recognition, in: Proceedings of the National Academy of Science 89, (1992) 60-64.
9. F.G. Callari, F.P. Ferrie, Autonomous recognition: Driven by ambiguity, in: Proceedings of the Conference on Computer Vision and Pattern Recognition, 1996, pp. 701-707.
10. V. Colin de Verdière, J.L. Crowley, Visual recognition using local appearance, in: Proceedings of the Fifth European Conference on Computer Vision, Freiburg, Germany, 1998.
11. B.A. Draper, K. Ikeuchi, M. Veloso, Learning control strategies for object recognition, in: Symbolic Visual Learning, Oxford University Press, New York, 1997, Chapter 3, pp. 49-76.
12. S. Edelman, Computational theories of object recognition, Trends in Cognitive Sciences, 1997.
13. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, 1990.
14. G.D. Hager, Task-Directed Sensor Fusion and Planning: A Computational Approach, Kluwer Academic Publishers, Boston, MA, 1990.
15. Kaelbling L.P., Moore A.W. Reinforcement learning: A survey. Journal of Artificial Intelligence Research. 4:1996;237-285.
16. D. Lowe, Radial basis function networks, in: M.A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, MIT Press, Cambridge, MA, 1995, pp. 779-782.
17. Moghaddam B., Pentland A. Probabilistic visual learning for object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 19(7):1997;696-710.
18. Murase H., Nayar S.K. Illumination planning for object recognition using parametric eigenspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence. 16(12):1994;1219-1227.
19. Murase H., Nayar S.K. Visual learning and recognition of 3D objects from appearance. International Journal of Computer Vision. 14(1):1995;5-24.
20. L. Paletta, Learning active object recognition, Ph.D. Thesis, Institute for Computer Graphics and Vision, Technical University Graz, Austria, 1999.
21. L. Paletta, E. Rome, A. Pinz, Visual object detection for autonomous sewer robots, in: Proceedings of the International Conference on Intelligent Robots and Systems, Kyongju, South Korea, 1999.
22. J. Pearl, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, San Francisco, CA, 1988.
23. Peng J., Bhanu B. Closed-loop object recognition using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(2):1998;139-154.
24. Poggio T., Edelman S. A network that learns to recognize three-dimensional objects. Nature. 317:1990;314-319.
25. M. Prantl, H. Borotschnig, H. Ganster, D. Sinclair, A. Pinz, Object recognition by active fusion, in: Proceedings of the SPIE 2904 (1996) 320-330.
26. M.L. Puterman, Markov Decision Processes, Wiley, New York, 1994.
27. R.P.N. Rao, Dynamic appearance-based recognition, in: Proceedings of the Conference on Computer Vision and Pattern Recognition, 1997, pp. 540-546.
28. Rimey R.D., Brown C.M. Control of selective perception using Bayes nets and decision theory. International Journal of Computer Vision. 12(2/3):1994;173-207.
29. E. Rivlin, Y. Aloimonos, A. Rosenfeld, Purposive recognition: An active and qualitative approach, in: Proceedings of the SPIE 1611 (1991) 225-240.
30. Rumelhart D., Hinton G., Williams R. Learning representations by back-propagating errors. Nature. 323:1986;533-536.
31. Schiele B., Crowley J.L. Transinformation of object recognition and its application to viewpoint planning. Robotics and Autonomous Systems. 21(1):1997;95-106.
32. Seibert M., Waxman A.M. Adaptive 3D object recognition from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence. 14(2):1992;107-124.
33. M.A. Sipe, D. Casasent, L. Neiberg, Feature space trajectory representation for active vision, in: Proceedings of the SPIE 3077 (1997) 254-265.
34. R.S. Sutton, A.G. Barto, Reinforcement Learning, MIT Press, Cambridge, MA, 1998.
35. Watkins C.J.C.H., Dayan P. Q learning. Machine Learning. 8(3):1992;279-292.
36. Whitehead S.D., Ballard D.H. Learning to perceive and act by trial and error. Machine Learning. 7:1991;45-83.