Preprocessing of video signals for MPEG coding by clustering filter
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Estimation of subpixel motion using bispectrum
Research Letters in Signal Processing
A robust subpixel motion estimation algorithm using HOS in the parametric domain
Journal on Image and Video Processing - Special issue on patches in vision
Robust temporal activity templates using higher order statistics
IEEE Transactions on Image Processing
Fast frequency template matching using higher order statistics
IEEE Transactions on Image Processing
| Hi-index | 0.01 |
A class of algorithms is presented that estimates the displacement vector from two successive image frames consisting of signal plus noise. In the model, the signals are assumed to be either non-Gaussian or (quasistationary) deterministic; and, via a consistency result for cumulant estimators, the authors unify the stochastic and deterministic signal viewpoints. The noise sources are assumed to be Gaussian (perhaps spatially and temporally correlated) and of unknown covariance. Viewing image motion estimation as a 2D time delay estimation problem, the displacement vector of a moving object is estimated by solving linear equations involving third-order auto-cumulants and cross-cumulants. Additionally, a block-matching algorithm is developed that follows from a cumulant-error optimality criterion. Finally, the displacement vector for each pel is estimated using a recursive algorithm that minimizes a mean 2D fourth-order cumulant criterion. Simulation results are presented and discussed