Recognising and segmenting objects in natural environments

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2006, Pages 5866-5871

  Ramos, Fabio T ^a   Upcroft, Ben ^a   Kumar, Suresh ^a   Durrant Whyte, Hugh F ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BAYESIAN NETWORKS; COMPUTATION THEORY; PARAMETER ESTIMATION;

DENSITY ESTIMATION PROBLEMS; DIMENSIONALITY REDUCTION TECHNIQUES; WAVELET CONVOLUTION;

OBJECT RECOGNITION;

EID: 34250684950     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2006.282463     Document Type: Conference Paper

References (24)

1. H. Attias. Inferring parameters and structure of latent variable models by variational bayes. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pages 21-30, San Francisco, USA, 1999. Morgan Kaufmann.
2. H. Attias. A variational bayesian framework for graphical models. In Proceedings of Neural Information Processing Systems 12, Cambridge, MA, USA, 2000. MIT Press.
3. M. J. Beal. Variational Algorithms for Approximate Bayesian inference. PhD thesis, The Gatsby Computational Neuroscience Unit, University College London, May 2003.
4. J. M. Bernardo and A. F. M. Smith. Bayesian Theory. Wiley, New York, USA, 1994.
5. P. Cheeseman and J. Stutz. Bayesian classification (Autoclass): Theory and results. Advances in Knowledge Discovery and Data Mining, pages 153-180, 1996.
6. T. M. Cover and J. A. Thomas. Elements of Information Theory. John Wiley & Sons, Inc, New York,USA, 1991.
7. R. Duda, P. Hart, and D. Stork. Pattern Classification. Wiley-Interscience, New York, USA, second edition, 2001.
8. L. Fei Fei, R. Fergus, and P. Perona. A Bayesian approach to unsupervised one-shot learning of object categories. In Proceedings of the International Conference on Computer Vision (ICCV), Nice, France, 2003.
9. D. J. Field. Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America, 4(12):2379-2394, 1987.
10. T. S. Jaakkola and M. I. Jordan. Bayesian parameter estimation via variational methods. Statistics and Computing, 10:25-37, 2000.
11. M. I. Jordan, Z. Ghahramani, T. Jaakkola, and L. K. Saul. An introduction to variational methods for graphical models. Machine Learning, 37(2): 183-233, 1999.
12. D. J. C. Mackay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge, UK, 2003.
13. T. P. Minka. Using lower bounds to approximate integrals. Informal notes available at http://www.stat.cmu.edu/minka/papers/learning.html, 2001.
14. R. J. Muirhead. Aspects of Multivariate Statistical Theory. Wiley, New York, USA, 1982.
15. D. Pelleg and A. Moore. X-means: Extending K-means with efficient estimation of the number of clusters. In Proc. 17th International Conf. on Machine Learning, pages 727-734. Morgan Kaufmann, San Francisco, CA, 2000.
16. S. J. Roberts, D. Husmeier, I. Rezek, and W. Penny. Bayesian approaches to Gaussian mixture modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11), November 1998.
17. G. Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6:461-464, 1978.
18. J. Suzuki. Learning Bayesian belief networks based on the MDL principle: An efficient algorithm using the branch and bound technique. IEICE Transactions on Information and Systems, E81-D(12), December 1998.
19. J. Tenenbaum, V. DeSilva, and J. C. Langford. A global geometric framework for nonlinear dimensionality redution. Science, 290:2319-2323, 2000.
20. A. Torralba, K. Murphy, and W. Freeman. Sharing features: efficient boosting procedures for multiclass object detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, USA, 2004.
21. I. Ulusoy and C. M. Bishop. Generative versus discriminative methods for object recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005.
22. B. Upcroft, S. Kumar, M. Ridley, S. Ong, and H. Durrant-Whyte. Fast Parameter Estimation for General Bayesian Filters in Robotics. In Proceedings of Australian Conf. on Robotics and Automation, 2004.
23. M. Vidal-Naquet and S. Ullman. Object recognition with informative features and linear classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2003.
24. P. Viola and M. Jones. Robust real-time object detection. International Journal of Computer Vision, 57(2):137-154, 2004.