ISSN:
1573-0824
Keywords:
image modeling
;
2-D noncausal models
;
AR models
;
texture modeling
;
image processing
Source:
Springer Online Journal Archives 1860-2000
Topics:
Electrical Engineering, Measurement and Control Technology
Notes:
Abstract In many one-dimensional (1-D) and two-dimensional (2-D) digital signal processing applications, auto-regressive (AR) models are very useful and powerful tools. Most of the development work done so far in 2-D AR modeling was limited to causal models. Recently, noncausal models have generated a great deal of interest because these models are a more natural choice for many applications in image processing. In this paper, we generalize the 1-D problems of noncausal linear-phase signal mdoeling and system modeling to their 2-D counterparts. The purpose of this paper is twofold. First, for homogeneous random fields, we introduce and investigate the 2-D symmetric (zero-phase) noncausal AR signal and system modeling problems. We then develop two computationally efficient algorithms for the determination of model parameters. Finally, we investigate an application in stochastic texture modeling and provide experimental results.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00986237
