Structure-based color learning on a mobile robot under changing illumination

Autonomous Robots

Volumn 23, Issue 3, 2007, Pages 161-182

  Sridharan, Mohan ^a   Stone, Peter ^a  

^a The University of Texas at Austin   (United States)

Author keywords

Color learning; Illumination invariance; Real time vision

Indexed keywords

DIGITAL CAMERAS; LIGHTING; MOBILE ROBOTS; SENSORY PERCEPTION; SONAR;

COLOR LEARNING; ILLUMINATION INVARIANCE; REAL-TIME VISION;

LEARNING ALGORITHMS;

EID: 34548507726     PISSN: 09295593     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10514-007-9038-7     Document Type: Article

References (55)

1. Anzani, F., Bosisio, D., Matteucci, M., & Sorrenti, D. G. (2005). Online color calibration in non-stationary environments. In The ninth international robocup symposium, Osaka, Japan, 18-19 July 2005.
2. Belongie, S., Malik, J., & Puzicha, J. (2002). Shape matching and object recognition using shape contexts. In Pattern analysis and machine intelligence.
3. Brainard, D. H., & Freeman, W. T. (1997). Bayesian color constancy. Journal of Optical Society of America A, 14(7), 1393-1411.
4. Brainard, D. H., & Wandell, B. A. (1986). Analysis of the retinex theory of color vision. Journal of Optical Society of America A, 3(10), 1651-1661.
5. Buchsbaum, G. (1980). A spatial processor model for object color perception. Journal of Franklin Institute, 310, 1-26.
6. Cameron, D., & Barnes, N. (2003), Knowledge-based autonomous dynamic color calibration, In The seventh international robocup symposium.
7. Chen, S., Siu, M., Vogelgesang, T., Yik, T. F., Hengst, B., Pham, S. B., & Sammut, C. (2002). RoboCup-2001: the fifth robocup competitions and conferences. Berlin: Springer.
8. Cohen, D., Ooi, Y. H., Vernaza, P., & Lee, D. D. (2004). RoboCup2003: the seventh robocup competitions and conferences. Berlin: Springer.
9. Comaniciu, D., & Meer, P. (2002). Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5), 603-619.
10. Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2001). Introduction to algorithms (2nd ed.). Cambridge: MIT.
11. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.
12. Duda, R. O., Hart, P. E., & Stork, D. G. (2000). Pattern classification. New York: Wiley.
13. Efron, B., & Tibshirani, R. J. (1993). An introduction to bootstrap. London: Chapman and Hall.
14. Farshidi, F., Sirouspour, S., & Kirubarajan, T. (2005). Active multi-camera object recognition in presence of occlusion, In The IEEE international conference on intelligent robots and systems (IROS).
15. Finlayson, G. (1996). Color in perspective. IEEE Transactions of Pattern Analysis and Machine Intelligence, 18(10), 1034-1038.
16. Finlayson, G., & Hordley, S. (2000). Improving gamut mapping color constancy. IEEE Transactions on Image Processing, 9(10), 11741183.
17. Finlayson, G., Hordley, S., & Hubel, P. (2001). Color by correlation: A simple, unifying framework for color constancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11).
18. Forsyth, D. (1990). A novel algorithm for color constancy. International Journal of Computer Vision, 5(1), 5-36.
19. Fred, A. L. N., & Jain, A. K. (2003). Robust data clustering, In The international conference of computer vision and pattern recognition (pp. 128-136), June 2003.
20. Gevers, T., & Smeulders, A. W. M. (1999). Color based object recognition. Pattern Recognition, 32(3), 453-464.
21. Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing. New York: Prentice Hall.
22. Hyams, J., Powell, M. W., & Murphy, R. R. (2000). Cooperative navigation of micro-rovers using color segmentation. Journal of Autonomous Robots, 9(1), 7-16.
23. Johnson, D. H., Gruner, C. M., Baggerly, K., & Seshagiri, C. (2001). Information-theoretic analysis of neural coding. Journal of Computational Neuroscience, 10, 47-69.
24. Jungel, M. (2004). Using layered color precision for a self-calibrating vision system. In The eighth international RoboCup symposium, Lisbon, Portugal.
25. Kitano, H., Asada, M., Noda, I., & Matsubara, H. (1998). Robot world cup. Robotics and Automation, 5(3), 30-36.
26. Land, E. H. (1977). The retinex theory of color constancy. Scientific American, 237, 108-129.
27. Lauziere, Y. B., Gingras, D., & Ferrie, F. P. (1999). Color camera characterization with an application to detection under daylight. In Vision Interface (VI) (pp. 280-287), May 1999.
28. Lauziere, Y. B., Gingras, D., & Ferrie, F. P. (1999). Autonomous physics-based color learning under daylight. In The EUROPTO conference on polarization and color techniques in industrial inspection (vol. 3826, pp. 86-100).
29. Least Squares Line Fitting, http://mathworld.wolfram.com/ LeastSquaresFitting.html.
30. Lenser, S., & Veloso, M. (2003). Automatic detection and response to environmental change. In The international conference of robotics and automation (ICRA), May 2003.
31. Lowe, D. (2004). Distinctive image features from scale-invariant key-points. International Journal of Computer Vision (IJCV), 60(2), 91-110.
32. Minten, B. W., Murphy, R. R., Hyams, J., & Micire, M. (2001). Low-order-complexity vision-based docking. IEEE Transactions on Robotics and Automation, 17(6), 922-930.
33. Montgomery, D. C., & Runger, G. C. (2002). Applies statistics and probability for engineers (3rd ed.). New York: Wiley.
34. Pantofaru, C., & Hebert, M. (2005). A comparison of image segmentation algorithms, cmu-ri-tr-05-40. Robotics Institute, Carnegie Mellon University, Tech. Rep., September 2005.
35. Pineau, J., Montemerlo, M., Pollack, M., Roy, N., & Thrun, S. (2003). Towards robotic assistants in nursing homes: challenges and results. In RAS special issue on socially interactive robots.
36. Rofer, T., & Jungel, M. (2003). Vision-based fast and reactive Monte-Carlo localization. In The IEEE international conference on robotics and automation (pp. 856-861), Taipei, Taiwan.
37. Rosenberg, C., Hebert, M., & Thrun, S. (2001). Color constancy using kl-divergence, In IEEE international conference on computer vision.
38. Rubner, Y., Tomasi, C., & Guibas, L. J. (2000). The earth mover's distance as a metric for image retrieval. International Journal of Computer Vision, 40(2), 99-121.
39. Schulz, D., & Fox, D. (2004). Bayesian color estimation for adaptive vision-based robot localization. In The IEEE international conference on intelligent robots and systems (IROS).
40. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation, In IEEE transactions on pattern analysis and machine intelligence (PAMI).
41. Sridharan, M., & Stone, P. (2004). Towards illumination invariance in the legged league. In The RoboCup symposium.
42. Sridharan, M., & Stone, P. (2005a). Autonomous color learning on a mobile robot, in The twentieth national conference on artificial intelligence (AAAI).
43. Sridharan, M., & Stone, P. (2005b). Real-time vision on a mobile robot platform. In The IEEE international conference on intelligent robots and systems (IROS).
44. Sridharan, M., & Stone, P. (2006). Autonomous planned color learning on a mobile robot without labeled data, In The ninth IEEE international conference on control, automation, robotics and vision (ICARCV), December 2006.
45. Sridharan, M., Kuhlmann, G., & Stone, P. (2005). Practical vision-based Monte Carlo localization on a legged robot. In The international conference on robotics and automation (ICRA), April 2005.
46. Stone, P., Dresner, K., Fidelman, P., Jong, N. K., Kohl, N., Kuhlmann, G., Lin, E., Sridharan, M., & Stronger, D. (2004). UT Austin Villa 2004: coming of age, TR 04-313. Department of Computer Sciences, UT-Austin, Tech. Rep., October 2004
47. Sumengen, B., Manjunath, B. S., & Kenney, C. (2003). Image segmentation using multi-region stability and edge strength, In The IEEE international conference on image processing (ICIP), September 2003.
48. Swain, M., & Ballard, D. H. (1991). Color indexing. International Journal of Computer Vision, 7(1), 11-32.
49. The Sony Aibo robots. (2004). http://www.sonystyle.com.
50. Thrun, S. (2006). Stanley: the robot that won the DARPA grand challenge. Journal of Field Robotics, 23(9), 661-692.
51. Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001). Robust Monte Carlo localization for mobile robots. Journal of Artificial Intelligence, 128(1), 99-141.
52. Torralba, A., Murphy, K. P., & Freeman, W. T. (2004). Sharing visual features for multiclass and multiview object detection, In The IEEE Conference on computer vision and pattern recognition (CVPR), Washington, DC.
53. Trichevsky, R. E., & Trofimov, V. K. (1981). The performance of universal coding. IEEE Transactions of Information Theory, 27,199-207.
54. Tsin, Y., Collins, R. T., Ramesh, V., & Kanade, T. (2001). Bayesian color constancy for outdoor object recognition. In IEEE pattern recognition and computer vision, December 2001.
55. Uther, W., Lenser, S., Bruce, J., Hock, M., & Veloso, M. (2001). Cm-pack'01: fast legged robot walking, robust localization, and team behaviors, In The fifth international robocup symposium, Seattle, USA.