A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in realworld imagery are discussed.

A new approach to the design of the feedback predictor in a two-dimensional (2-D) DPCM encoder is presented. This approach is based upon an assumed stochastic image model which exhibits the predominant and pronounced edge structure typical of real-world imagery and is in distinction from the conventional approach based upon 2-D autoregressive (AR) modeling assumptions. The performance of the resulting DPCM encoder is shown to offer significant performance advantages over existing DPCM design approaches at transmission rates of 2 bits/pixel or less. Furthermore, this new approach results in improved ability to cope with channel errors.

A two-dimensional homogeneous and isotropic random field is described which is generated by a polygonal partition process on the plane. In particular, the plane is divided into elementary disjoint polygonal regions by a Poisson line process. Gray levels within these elementary polygonal regions are assigned to have specified correlation wilh gray levels in contiguous regions. Explicit evaluation of the resulting autocorrelation function and power spectral density is provided. Several applications are described.

The problem of image enhancement by nonlinear two-dimensional (2-D) homomorphic filtering is approached using stochastic models of the signal and degradations. Homomorphic filtering has been used previously for image enhancement but the linear filtering operation has generally been chosen heuristically. In this paper stochastic image models described and analyzed previously by the authors are used to model the true image and interferring components (shadows and salt-and-pepper noise). The problem of designing the linear operation can then be formulated as one of linear least mean-squared error (Wiener) filtering. Examples of processing on typical real-world images are included to indicate the results obtainable.