Automatic detection and response to environmental change

Proceedings - IEEE International Conference on Robotics and Automation

Volumn 1, Issue , 2003, Pages 1416-1421

  Lenser, Scott ^a   Veloso, Maneula ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COLOR; COMPUTER VISION; MATHEMATICAL MODELS; PROBABILITY DISTRIBUTIONS; SENSORS;

CAMERA IMAGE; UNDERUTILIZED SENSORS;

ROBOTS;

EID: 0344013426     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. M. Basseville and I. Nikiforov. Detection of Abrupt Change - Theory and Application. Prentice-Hall, Englewood Cliffs, N.J., 1993.
2. J. Bruce, T. Balch, and M. Veloso. CMVision (http://www.coral.cs.cmu.edu/cmvision/).
3. J. Bruce, T. Balch, and M. Veloso. Fast and inexpensive color image segmentation for interactive robots. In Proceedings of IROS-2000, 2000.
4. K. Deng, A. Moore, and M. Nechyba. Learning to recognize time series: Combining arma models with memory-based learning. In IEEE Int. Symp. on Computational Intelligence in Robotics and Automation, volume 1, pages 246-250, 1997.
5. Z. Ghahramani and G. E. Hinton. Switching state-space models. Technical report, 6 King's College Road, Toronto M5S 3H5, Canada, 1998.
6. J. Kohlmorgen and S. Lemm. An on-line method for segmentation and identification of non-stationary time series. In NNSP 2001: Neural Networks for Signal Processing XI, pages 113-122, 2001.
7. N. Kuiper. In Proceedings of the Koninklijke Nederlandse Akademie van Wetenschappen, ser. A, volume 63, pages 38-47, 1962.
8. M. Likhachev, M. Kaess, and R. C. Arkin. Learning behavioral parameterization using spatio-temporal case-based reasoning. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), volume 2, pages 1282-1289, 2002.
9. F. Lanåker and L. Niklasson. Time series segmentation using an adaptive resource allocating vector quantization network based on change detection. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), pages 323-328, 2000.
10. S. Marsland. On-line Novelty Detection Through Self-Organization, with Application to Inspection Robotics. PhD thesis, University of Manchester, 2001.
11. W. Penny and S. Roberts. Dynamic models for nonstationary signal segmentation. Computers and Biomedical Research, 32(6):483-502, 1999.