Modeling of video sequences by Gaussian mixture: application in motion estimation by block matching method

Authors:
Abdenaceur Boudlal;Benayad Nsiri;Driss Aboutajdine
Affiliations:
LRIT Laboratory, CNRST, Faculty of Science, University Mohammed V-Agdal, Rabat, Morocco;LPMMAT Faculty of Science Ain Chock, University Hassan II Ain Chock, Casablanca, Morocco;LRIT Laboratory, CNRST, Faculty of Science, University Mohammed V-Agdal, Rabat, Morocco
Venue:
EURASIP Journal on Advances in Signal Processing
Year:
2010

Citing 8
Cited 0

Derivation of optical flow using a spatiotemporal-frequency approach

Computer Vision, Graphics, and Image Processing
A view of the EM algorithm that justifies incremental, sparse, and other variants

Learning in graphical models
Reliable Estimation of Dense Optical Flow Fields with Large Displacements

International Journal of Computer Vision
Motion Analysis for Image Sequence Coding

Motion Analysis for Image Sequence Coding
On convergence properties of the em algorithm for gaussian mixtures

Neural Computation
Digital image stabilization with sub-image phase correlation based global motion estimation

IEEE Transactions on Consumer Electronics
Bayesian pixel classification using spatially variant finite mixtures and the generalized EM algorithm

IEEE Transactions on Image Processing
Adaptive motion tracking block matching algorithms for video coding

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by amixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices) are estimated by the Expectation Maximization algorithm (EM) which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.