Title :
Parameter estimation for two-dimensional vector models using neural networks
Author :
Xu, Lin ; Azimi-Sadjadi, Mahmood R.
Author_Institution :
Eastman Kodak Co., Billerica, MA, USA
fDate :
12/1/1995 12:00:00 AM
Abstract :
This correspondence addresses the problem of two-dimensional (2-D) vector image model parameter estimation using a new recursive least squares (RLS)-based learning method. Vector autoregressive (AR) models with various 1-D and 2-D, causal and noncausal regions of support (ROS) are considered. Numerical results are presented which demonstrate the usefulness of the proposed scheme for on-line implementation
Keywords :
autoregressive processes; convergence of numerical methods; image processing; learning (artificial intelligence); least squares approximations; neural nets; parameter estimation; vectors; 2D image model; AR models; autoregressive models; causal regions of support; neural networks; noncausal regions of support; numerical results; on-line implementation; parameter estimation; recursive least squares-based learning method; two-dimensional vector model; Circuits; Delta modulation; Digital filters; Digital signal processing; Least squares approximation; Neural networks; Parameter estimation; Pixel; Signal processing algorithms; Speech processing;
Journal_Title :
Signal Processing, IEEE Transactions on
