Robust optical flow estimation using semi-invariant local features

Pattern Recognition

Volumn 30, Issue 2, 1997, Pages 229-237

  Ghosal, Sugata ^a   Mehrotra, Rajiv ^b  

^a Amherst Systems Inc   (United States)

^b UNIVERSITY OF MISSOURI ST LOUIS   (United States)

Author keywords

Dynamic scene analysis; Image sequence analysis; Invariant features; Motion estimation; Optical flow; Orthogonal Zernike moments

Indexed keywords

ALGORITHMS; IMAGE ANALYSIS; INVARIANCE; ROBUSTNESS (CONTROL SYSTEMS);

OPTICAL FLOW; SEMI INVARIANT LOCAL FEATURES;

OPTICAL FLOWS;

EID: 0031061869     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(96)00070-2     Document Type: Article

References (16)

1. J. K. Aggarwal and N. Nandhakumar, On the computation of motion from sequences of images - A review, Proc. IEEE 76(8), 917-935 (1988).
2. B. K. P. Horn and B. G. Schunck, Determining optic flow, Artificial Intell. 17, 185-203 (1981).
3. H. H. Nagel, Displacement vectors derived from second-order intensity variations in image sequence, Computing Vision Graphics Image Process. 21, 85-117 (1983).
4. R. M. Haralick and J. S. Lee, The facet approach to optic flow, Proc. Image Understanding Workshop, Arlington, Virginia, 84-93 (1983).
5. O. Tretiak and L. Pastor, Velocity estimates from image sequences with second-order differential operators, Proc. Int. Conf. Pattern Recognition, 16-19 (1984).
6. M. Campani and A. Verri, Computing optic flow from an overconstrained system of linear algebraic equations, Proc. Int. Conf. Computer Vision, 22-26 (1987).
7. B. Lucas, Ph.D. Dissertation, Carnegie-Mellon University (1984).
8. E. Simoncelli, E. Adelson and D. Heeger, Probability distributions of optical flow, Proc. IEEE Conf. Computer Vision Pattern Recognition, 310.0515 (1991).
9. A. Mitiche, Y. F. Wang and J. K. Aggarwal, Experiments in computing optic flow with the gradient-based, multi-constraint method, Pattern Recognition 20(2), 173-179 (1987).
10. K. Wohn, L. S. Davis and P. Thrift, Motion estimation based on multiple local constraints and non-linear smoothings, Pattern Recognition 16(6), 567-570 (1983).
11. V. Markandey and B. E. Flinchbaugh, Multispectral constraints for optical flow computation, Proc. Int. Conf. Computer Vision, 38-41 (1987).
12. R. J. Woodham, Multiple light source optical flow, Int. Conf. Computer Vision, 42-46 (1987).
13. S. Ghosal and R. Mehrotra, Orthogonal moments for subpixel edge detection, Pattern Recognition 26(2), 295-306 (1993).
14. S. Ghosal and R. Mehrotra, Detection of composite edges, IEEE Trans. Image Process. 3(1), 14-25 (1994).
15. S. Ghosal and R. Mehrotra, Segmentation of range images: An orthogonal moment-based integrated approach, IEEE Trans. Robotics Automation 9(4), 385-399 (1993).
16. J. L. Barron et al., Performance of optical flow technique, Proc. IEEE Conf. Computer Vision, Pattern Recognition, 236-242 (1992).