Real-time quantized optical flow

Real-Time Imaging

Volumn 3, Issue 2, 1997, Pages 71-86

  Camus, Ted ^a  

^a NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER HARDWARE; COMPUTER WORKSTATIONS; IMAGE PROCESSING; OPTICAL FLOWS; PERFORMANCE; REAL TIME SYSTEMS; ROBOTICS;

QUANTIZATION; SCIENTIFIC WORKSTATION;

COMPUTER VISION;

EID: 0031126036     PISSN: 10072014     EISSN: None     Source Type: Journal    
DOI: 10.1006/rtim.1996.0048     Document Type: Article

References (39)

1. Nelson, R. & Aloimonos, J. (1987) Obstacle avoidance using flow field divergence, IEEE PAMI-11, 10: 1102-1106.
2. Weber, J. & Malik, J. (1993) Robust computation of optical flow in a multi-scale differential framework, Proc. the Fourth International Conference on Computer Vision, pp. 12-20.
3. Patterson, D. & Hennessy, J. (1994) Computer Organization and Design: the Hardware/Software Interface, San Mateo, CA: Morgan Kaufmann.
4. Horn, B. & Schunck, P. Determining optical flow, Artificial Intelligence, 17: 185-203.
5. Huang, L. & Aloimonis, Y. (1991) Relative depth from motion using normal flow: an active and purposeful solution, Proc. of the IEEE Workshop on Visual Motion, pp. 196-204.
6. Coombs, D., Herman, M., Hong, T. & Nashman, M. (1995) Real-time obstacle avoidance using central flow divergence and peripheral flow, Proc. of the Fifth International Conference on Computer Vision, Cambridge, MA.
7. Nishihara, H. (1984) Practical real-time imaging stereo matcher, Optical Engineering, 23(5): 536-545.
8. Bülthoff, H., Little, J. & Poggio, T. (1989) A parallel algorithm for real-time computation of optical flow, Nature, 337(6207): 549-553.
9. Del Bombo, A. & Nesi, P. (1993) Optical flow estimation on the connection machine 2, pp. 267-274, Proc. of the IEEE Workshop on Computer Architectures for Machine Perception, New Orleans, Lousiana, Los Alamitos, CA: IEEE Computer Society Press.
10. Little, J. &Verri, A. (1989) Analysis of differential and matching methods for optical flow, Proc. of the IEEE 1989 Workshop on Visual Motion, pp. 173-180.
11. Barron, J., Fleet, D. & Beauchemin, S.S. (1994) Performance of optical flow techniques, Int. J. Computer Vision, 12(1): 43-77.
12. Liu, H., Hong, T., Herman, M. & Chellapa, R. (1996) Accuracy vs. Efficiency trade-offs in optical flow algorithms, Proc. of the Fourth European Conference on Computer Vision, Cambridge, England.
13. Dutta, R. & Weems, C. (1993) Parallel dense depth from motion on the image understanding architecture, Proc. the IEEE 1993 CVPR, pp. 154-159.
14. Little, J. & Kahn, J. (1993) A smart buffer for tracking using motion data, pp. 257-266, Proc. of the IEEE Workshop on Computer Architecture for Machine Perception, New Orleans, Lousiana, Los Alamitos, CA: IEEE Computer Society Press.
15. Bülthoff, H., Little, J. & Poggio, T. (1989) A parallel motion algorithm consistent with psychophysics and physiology, Proc. of the IEEE 1989 Workshop on Visual Motion, pp. 165-172.
16. Camus, T. & Bülthoff, H. (1991) Space-time tradeoffs for adaptive real-time tracking, Proc. of the SPIE Mobile Robots VI Conference 1613, W.J. Wolfe, W.H. Chun (eds), pp. 268-276.
17. Camus, T. (1994) Real-Time Optical Flow, PhD Thesis, Brown University Technical Report CS-94-36.
18. Camus, T. (1994) Real-Time Optical Flow, SME Technical Paper MS94-176, MVA/SME Applied Machine Vision June, Minneapolis, Minnesota.
19. Reichardt, W. (1957) Autokorrelationsauswertung als Funktionsprinzip des Zentralnervensystems, Z. Naturforsch, 12b: 447-457.
20. Anandan, P. (1989) A computational framework and an algorithm for the measurement of visual motion, Int. J. Computer Vision, 2: 283-310.
21. Nalwa, V. (1993) A Guided Tour of Computer Vision, Reading, MA: Addison-Wesley.
22. Tresilian, J. (1991) Empirical and theoretical issues in the perception of time to contact, J. Experimental Psychology: Human Perception and Performance, 17(3): 865-876.
23. Duchon, A. & Warren, W. (1994) Robot navigation from a Gibsonian viewpoint, Proc. of the IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX. October 2-5. IEEE, Piscataway, NJ, pp. 2272-2277.
24. Camus, T. (1995) Calculating time-to-contact using real-time quantized optical flow, National Institute of Standards and Technology NISTIR 5609, March.
25. McCann, J. (1995) Neural Networks for Mobile Robot Navigation, Master Thesis, Brown University, February.
26. Duchon, A., Warren, W. & Kaelbling, L. (1995) Ecological robotics: controlling behavior with optical flow, pp. 164-169, Proc. of the Seventeenth Annual Conference of the Cognitive Science Society, Pittsburg, PA, July 22-25. D. Moore and J. Fain Lehman, eds. Mahwah, NJ: Lawrence Erlbaum Associates.
27. Warren, W. Jr. (1988) Action modes and laws of control for the visual guidance of action. In Complex Movement Behavior: The motor-action controversy, O. Meijer & K. Roth, eds., North-Holland: Elsevier Science, B.V., pp. 339-380.
28. Camus, T., Coombs, D., Herman, M. & Hong, T. (1996) Realtime single-workstation obstacle avoidance using only wide-field flow divergence, Proc. of the Int. Conf. on Pattern Recognition, 3: 323-330, Vienna, Austria, August.
29. Barron, J., Fleet, D., Beauchemin, S.S. & Burkitt, T. (1992) Performance of optical flow techniques, Proc. of the IEEE 1992 CVPR, pp. 236-242.
30. Bober, M. & Kittler, J. (1994) Robust motion analysis, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, pp. 947-952.
31. Uras, S., Girosi, F., Verri, A. & Torre, V. (1988) A computational approach to motion perception, Biological Cybernetics, 60: 79-87.
32. Liu, H. (1995) A general motion model and spatio-temporal filters for 3-D motion interpretations, PhD. Thesis, Technical Report CAR-TR-789 and CS-TR-3525, University of Maryland.
33. Liu, H., Hong, T., Herman, M. & Chellapa, R. (1996) A general motion model and spatio-temporal filters for computing optical flow, to appear in International Journal of Computer Vision.
34. Camus, T. (1995) Real-time quantized optical flow, Proc. of the IEEE Workshop on Computer Architectures for Machine Perception, Como, Italy, September 18-20, Los Alamitos, CA: IEEE Computer Society Press.
35. Fleet, D. & Jepson, A. (1990) Computation of component image velocity from local phase information, Int. J. Computer Vision, 5(1): 77-104.
36. Lucas, B. & Kanade, T. (1981) An iterative image registration technique with an application to stereo vision, Proc. of the DARPA Image Understanding Workshop, pp. 121-130.
37. Lin, C.-C., Prasanna, V. & Khokhar, A. (1993) Scalable parallel extraction of linear features on MP-2, pp. 352-361, Proc. of the IEEE Workshop on Computer Architectures for Machine Perception, New Orleans, Louisiana, Los Alamitos, CA: IEEE Computer Society Press.
38. Camus, T. (1991) Accelerating optical flow computations in VLSI, unpublished report, Brown University.
39. Chakrabarti, C. & Jaja, J. (1991) VLSI architectures for template matching and block-matching. In Parallel Architectures and Algorithms for Image Understanding, V.K.P. Kumar, ed., pp. 3-27, Academic Press.