Efficient discriminant viewpoint selection for active Bayesian recognition

International Journal of Computer Vision

Volumn 68, Issue 3, 2006, Pages 267-287

  Laporte, Catherine ^a   Arbel, Tal ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

Active vision; Bayesian inference; Efficient viewpoint selection; Object recognition; Pose estimation

Indexed keywords

ALGORITHMS; COMPUTATION THEORY; INFORMATION ANALYSIS; PARAMETER ESTIMATION;

ACTIVE VISION; BAYESIAN INFERENCE; EFFICIENT VIEWPOINT SELECTION; POSE ESTIMATION;

OBJECT RECOGNITION;

EID: 33646881517     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-005-4436-9     Document Type: Article

References (34)

1. Aloimonos, J., Weiss, I., and Bandyopadhyay, A. 1988. Active vision. International Journal of Computer Vision, 1(4):333-356.
2. Arbel, T. and Ferrie, P.P. 2001. Entropy-based gaze planning. Image and Vision Computing, 19:779-786.
3. Arbel, T. and Ferrie, F.P. 2001. On the sequential accumulation of evidence. International Journal of Computer Vision, 43(3):205-230.
4. Arbel, T. and Ferrie, F.P. 2002. Interactive visual dialog. Image and Vision Computing, 20:639-646.
5. Bajcsy, R. 1988. Active perception. Proceedings of the IEEE, 76(8):966-1005.
6. Borotschnig, H., Paletta, L., Prantl, M., and Pinz, A. 2000. Appearance-based active object recognition. Image and Vision Computing, 18:715-727.
7. Callari, F.G. and Ferrie, F.P. 2001. Active object recognition: Looking for differences. International Journal of Computer Vision, 43(3): 189-204.
8. Chen, J.-H. and Chen, C.-S. 2004. Object recognition based on image sequences by using inter-feature-line consistencies. Pattern Recognition, 37:1913-1923.
9. Cover, T.M. and Thomas, J. A. 1991. Elements of Information Theory, Wiley.
10. Darrell, T. and Pentland, A. 1995. Active gesture recognition using learned visual attention. In Neural Information Processing Systems, vol. 8, pp. 858-864.
11. Denzler, J. and Brown, C.M. 2002. Information theoretic sensor data selection for active object recognition and state estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2): 145-157.
12. Dickinson, S.J., Christensen, H.I., Tsotsos, J.K., and Olofsson, G. 1997. Active object recognition integrating attention and viewpoint control. Computer Vision and Image Understanding, 67(3):239-260.
13. Eitin, E. and Priddy, K.L. 2002. Reinforcement learning and design of nonparametric sequential decision networks. In Proceedings of the SPIE Conference on Applications and Science of Computational Intelligence IV.
14. Geman, D. and Jedynak, B. 2001. Model-based classification trees. IEEE Transactions on Information Theory, 47(3): 1075-1082.
15. Glover, F. and Laguna, M. 1998. Tabu Search. Kluwer Academic Publishers.
16. Goldberger, J., Gordon, S., and Greenspan, H. 2003. An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures. In Proceedings of the 9th International Conference on Computer Vision, Nice, France, pp. 487-493.
17. Gremban, K.D. and Ikeuchi, K. 1994. Planning multiple observations for object recognition. International Journal of Computer Vision, 12(2): 137-172.
18. Herbin, S. 1996. Recognizing 3D objects by generating random actions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 35-40,
19. Kaelbling, L.P., Littman, M.L., and Cassandra, A.R. 1998. Planning and acting in partially observable stochastic domains. Artificial Intelligence, 101:99-134.
20. Kovačič, S., Leonardis, A., and Pernuš, F. 1998. Planning sequences of views for 3-D object recognition and pose determination, Pattern Recognition, 31(10): 1407-1417.
21. Laporte, C. 2004. A fast discriminant approach to active Bayesian visual recognition and pose estimation. Master's thesis, McGill University, Montreal, Quebec, Canada.
22. Laporte, C., Brooks, R., and Arbel, T. 2004. A fast discriminant approach to active object recognition and pose estimation. In Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, U.K, vol. 3, pp. 91-94,
23. MacKay, D.J.C. 1992. Bayesian interpolation. Neural Computation, 4(3):415-447.
24. MacKay, D.J.C. 1992. Information-based objective functions for active data selection. Neural Computation, 4(4):589-603.
25. Murase, H. and Nayar, S.K. 1995. Visual learning and recognition of 3-D objects from appearance. International Journal of Computer Vision, 14:5-24.
26. Paletta, L. and Pinz, A. 2000. Active object recognition by view integration and reinforcement learning. Robotics and Autonomous Systems, 31:71-86.
27. Paletta, L., Prantl, M., and Pinz, A. 2000. Learning temporal context in active object recognition using Bayesian analysis. In Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, pp. 695-699.
28. Russel, S. and Norvig, P. 1995. Artificial Intelligence: A Modern Approach, Prentice-Hall.
29. Schiele, B. and Crowley, J.L. 1998. Transinformation for active object recognition. In Proceedings of the International Conference on Computer Vision, Bombay, India, pp. 249-254.
30. Seibert, M. and Waxman, A.M. 1992. Adaptive 3-D object recognition from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2): 107-124.
31. Sipe, M.A. and Casasent, D. 2002. Feature space trajectory methods for active computer vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12):1634-1643.
32. Sutton, R.S. and Barto, A.G. 1998. Reinforcement Learning: An Introduction. MIT Press: Cambridge, MA.
33. Vasconcelos, N. 2001. On the complexity of probabilistic image retrieval. In Proceedings of the 8th International Conference on Computer Vision. Vancouver, Canada., pp. 400-407.
34. Zhou, X.S., Comaniciu, D., and Krishnan, A. 2003. Conditional feature sensitivity: A unifying view on active recognition and feature selection. In Proceedings of the 9th International Conference on Computer Vision, Nice, France, pp. 1502-1509.