Discrete Wavelet Analysis for Fast Optic Flow Computation

@inproceedings{Bernard1999DiscreteWA,
  title={Discrete Wavelet Analysis for Fast Optic Flow Computation},
  author={Christophe P. Bernard},
  year={1999}
}
This paper describes a new way to compute the optic flow, based on a discrete wavelet basis analysis. The optic flow is estimated locally by the projection of the di fferential optic flow equation onto wavelets. The resulting linear systems are small and of fixed size (3-5 e quations). They are solved to find out the visual displacement. In this way, we circumvent the classical prob lems of time aliasing and aperture. Moreover, the coefficients of the systems can be computed with a set of wa… CONTINUE READING

Citations

Publications citing this paper.
Showing 1-10 of 27 extracted citations

Cardiac motion analysis using wavelet projections from tagged MR sequences

2014 IEEE International Conference on Image Processing (ICIP) • 2014
View 4 Excerpts
Highly Influenced

Rigid Motion Model for Audio Source Separation

IEEE Transactions on Signal Processing • 2016
View 4 Excerpts
Highly Influenced

Audio source separation with time-frequency velocities

2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) • 2014
View 3 Excerpts
Highly Influenced

An Overview of Lead and Accompaniment Separation in Music

IEEE/ACM Transactions on Audio, Speech, and Language Processing • 2018

Acoustic classification and tracking of multiple targets using wireless sensor networks

2015 15th International Conference on Control, Automation and Systems (ICCAS) • 2015
View 1 Excerpt

Rate-distortion optimized optical flow estimation

2015 IEEE International Conference on Image Processing (ICIP) • 2015
View 1 Excerpt

(is-cfar) -aided Complex Wavelet De-noising

Hussein M. Hathal, Sarmad K. Ibrahim, Al-Mustansiriya
2014
View 1 Excerpt

References

Publications referenced by this paper.
Showing 1-10 of 27 references

Motion estimation using a complex-valued wavelet transform

IEEE Trans. Signal Processing • 1998
View 13 Excerpts
Highly Influenced

Determining Optical Flow

Artif. Intell. • 1981
View 6 Excerpts
Highly Influenced

Bayesian multi–scale differentia l optical flow. In Haussecker Jähne and Geissler, editors,Handbook of computer vision and applications

Eero P. Simoncelli
1998
View 8 Excerpts
Highly Influenced

Computation of component image velocity from local phase information

International Journal of Computer Vision • 1990
View 4 Excerpts
Highly Influenced

Multifrequency channel decompositions of images and wavelet models

IEEE Trans. Acoustics, Speech, and Signal Processing • 1989
View 5 Excerpts
Highly Influenced

A model of neuron al response in visual aera MT. Vision Research

Eero P. Simoncelli, David J. Heeger
1998

Similar Papers

Loading similar papers…