Bispectrum signal processing on HNC's SIMD numerical array processor (SNAP)
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
2-D signal modelling and reconstruction using third-order cumulants
Signal Processing
Cross-Bispectrum Computation and Variance Estimation
ACM Transactions on Mathematical Software (TOMS)
Robust Block-matching Motion-estimation Technique for Noisy Sources
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
Motion estimation and compensation from noisy image sequences: A new filtering scheme
Image and Vision Computing
Estimation of subpixel motion using bispectrum
Research Letters in Signal Processing
Motion estimation using higher order statistics
IEEE Transactions on Image Processing
Image motion estimation algorithms using cumulants
IEEE Transactions on Image Processing
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Motion estimation techniques are widely used in todays video processing systems. The most frequently used techniques are the optical flow method and phase correlation method. The vast majority of these algorithms consider noise-free data. Thus, in the case of the image sequences are severely corrupted by additive Gaussian (perhaps non-Gaussian) noises of unknown covariance, the classical techniques will fail to work because they will also estimate the noise spatial correlation. In this paper, we have studied this topic from a viewpoint different from the above to explore the fundamental limits in image motion estimation. Our scheme is based on subpixel motion estimation algorithm using bispectrum in the parametric domain. The motion vector of a moving object is estimated by solving linear equations involving third-order hologram and the matrix containing Dirac delta function. Simulation results are presented and compared to the optical flow and phase correlation algorithms; this approach provides more reliable displacement estimates particularly for complex noisy image sequences. In our simulation, we used the database freely available on the web.