Reliable fiducial detection in natural scenes

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3024, Issue , 2004, Pages 469-480

  Claus, David ^a   Fitzgibbon, Andrew W ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER VISION; FACE RECOGNITION;

COMPUTATIONAL COSTS; DETECTION PROBLEMS; DETECTION SCHEME; IMAGING CONDITIONS; MACHINE LEARNING TECHNIQUES; NATURAL SCENES; REAL-WORLD IMAGE; RELIABLE DETECTION;

LEARNING SYSTEMS;

EID: 34247557491     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24673-2_38     Document Type: Article

References (28)

1. Klein, G., Drummond, T.: Tightly integrated sensor fusion for robust visual tracking. In: Proc. BMVC. Volume 2. (2002) 787-796
2. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. CVPR. (2001)
3. Neumann, U., Bajura, M.: Dynamic registration correction in augmented-reality systems. In: IEEE Virtual Reality Annual Int'l Symposium. (1995) 189-196
4. Welch, G., Bishop, G., et al.: The HiBall tracker: High-performance wide-area tracking for virtual and augmented environments. In: Proc. ACM VRST. (1999) 1-10
5. Mellor, J.P.: Enhanced reality visualization in a surgical environment. A.I. Technical Report 1544, MIT, Artificial Intelligence Laboratory (1995)
6. State, A., et al.: Superior augmented reality registration by integrating landmark tracking and magnetic tracking. In: SIGGRAPH. (1996) 429-438
7. Cho, Y., Neumann, U.: Multi-ring color fiducial systems for scalable fiducial tracking augmented reality. In: Proc. of IEEE VRAIS. (1998)
8. Scharstein, D., Briggs, A.: Real-time recognition of self-similar landmarks. Image and Vision Computing 19 (2001) 763-772
9. Rekimoto, J., Ayatsuka, Y.: Cybercode: Designing augmented reality environments with visual tags. In: Proceedings of DARE. (2000)
10. Kato, H., Billinghurst, M.: Marker tracking and hmd calibration for a video-based augmented reality conferencing system. In: Int'l Workshop on AR. (1999) 85-94
11. Owen, C., Xiao, F., Middlin, P.: What is the best fiducial? In: The First IEEE International Augmented Reality Toolkit Workshop. (2002) 98-105
12. de Ipina, D.L., et al.: Trip: a low-cost vision-based location system for ubiquitous computing. Personal and Ubiquitous Computing 6 (2002) 206-219
13. Naimark, L., Foxlin, E.: Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker. In: ISMAR. (2002)
14. Efrat, A., Gotsman, C.: Subpixel image registration using circular fiducials. International Journal of Computational Geometry and Applications 4 (1994) 403-422
15. Bruckstein, A.M., Holt, R.J., Huang, T.S., Netravali, A.N.: New devices for 3D pose estimation: Mantis eyes, Agam paintings, sundials, and other space fiducials. International Journal of Computer Vision 39 (2000) 131-139
16. Wellner, P.: Adaptive thresholding for the digital desk. Technical Report EPC-1993-110, Xerox (1993)
17. Malbezin, P., Piekarski, W., Thomas, B.: Measuring artoolkit accuracy in long distance tracking experiments. In: 1st Int'l AR Toolkit Workshop. (2002)
18. Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Information Theory 13 (1967) 57-67
19. Ripley, B.D.: Why do nearest-neighbour algorithms do so well? SIMCAT (Similarity and Categorization), Edinburgh (1997)
20. Wilson, D.R., Martinez, T.R.: Reduction techniques for instance-based learning algorithms. Machine Learning 38 (2000) 257-286
21. Sproull, R.F.: Refinements to nearest-neighbor searching in k-dimensional trees. Algorithmica 6 (1991) 579-589
22. Berchtold, S., Ertl, B., Keim, D.A., Kriegel, H.P., Seidl, T.: Fast nearest neighbor search in high-dimensional spaces. In: Proc. ICDE. (1998) 209-218
23. Hart, P.: The condensed nearest neighbor rule. IEEE Trans. Information Theory 14 (1968) 515-516
24. Duda, R., Hart, P., Stork, D.: Pattern Classification. 2nd edn. John-Wiley (2001)
25. Cost, S., Salzberg, S.: A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning 10 (1993) 57-78
26. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th ALVEY Vision Conference. (1988) 147-151
27. Claus, D.: Video-based surveying for large outdoor environments. First Year Report, University of Oxford (2004)
28. http://www.robots.ox.ac.uk/∼dclaus/eccv/fiddetect.mpg.