Title :
Fine estimators of two-dimensional parameters and application to spatial shift estimation
Author_Institution :
INFOCOM Dept., Rome Univ., Italy
fDate :
12/1/1999 12:00:00 AM
Abstract :
This paper presents a fast technique for fine estimation of two-dimensional (2-D) parameters, based on a parabolic interpolation of the same ambiguity function samples, and aimed at block-oriented estimation of the spatial shift between pairs of images in video sequences. Expressions for the bias and variance of the position error and the prediction error are derived. The method is tested using a synthetically generated autocorrelation function, varying the directionality and the eccentricity factor, in order to compare the performance of the proposed 2-D estimator to the case of two separate one-dimensional (1-D) estimators. The method has also been applied in vision systems, evidencing encouraging results for estimating the parameters of sophisticated global motion models from real images
Keywords :
computer vision; correlation methods; error analysis; image motion analysis; image sampling; image sequences; interpolation; parameter estimation; prediction theory; video signal processing; 1D estimator; 2D parameter estimation; ambiguity function samples; block-oriented estimation; eccentricity factor; fast technique; fine estimation; global motion models; parabolic interpolation; position error bias; position error variance; prediction error; real images; spatial shift; spatial shift estimation; synthetically generated autocorrelation function; video sequences; vision systems; Displacement measurement; Image storage; Interpolation; Machine vision; Maximum likelihood estimation; Motion analysis; Motion estimation; Parameter estimation; Testing; Video sequences;
Journal_Title :
Signal Processing, IEEE Transactions on
