Performance of optical flow techniques
International Journal of Computer Vision
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
An Efficient VLSI Architecture for Full-Search Block MatchingAlgorithms
Journal of VLSI Signal Processing Systems
Journal of Signal Processing Systems
A new framework for complex wavelet transforms
IEEE Transactions on Signal Processing
Motion estimation using a complex-valued wavelet transform
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
In this paper, we introduce an algorithm for motion estimation. It combines complex wavelet decomposition and a fast motion estimation method based on affine model. The principle of wavelet transform is to decompose hierarchically the input image into a series of successively lower resolution reference images and detail images which contain the information needed to be reconstructed back to the next higher resolution level. The motion estimation determines the velocity field between two successive images. This phase can be extracted from this measure descriptive information of the sequence. Motion Estimation (ME) is an important part of any video compression system, since it can achieve significant compression by exploiting the temporal redundancy existing in a video sequence. This paper described a method from calculating the optical flow of an image sequence based on complex wavelet transform. It consists to project the optical flow vectors on a basis of complex-valued wavelets. Thus, we add an additional assumption on the shape of the velocity field that we want to find, which is the affinity of the optical flow. The two-dimensional affine motion model is used to formulate the optical flow problem by coarse resolution simultaneously coarse-and-fine, beside the traditional approach by coarse-to-fine, to avoid the error propagation during the decomposition of coarse level to fine level. This method opens the way for a quick and low-cost computing optical flow.