Toward a Structural Textural Analyzer Based on Statistical Methods

Abstract This paper reports investigations aimed at developing a feature set for the Spatial Gray Level Dependence Method (SGLDM) which measures visually perceivable qualities of textures. In particular it will be shown that the inertia measure commonly used with the SGLDM can be used to characterize the placement rules and the unit pattern of periodic textures. In this way one may formulate a structural approach to texture analysis based on the statistical SGLDM. To mathematically verify that the features used with the SGLDM can be used to characterize the unit pattern and placement rules of a periodic texture, a mathematical tiling theory model is proposed. This model allows one to develop the mathematical machinery necessary to prove the result. In a companion paper other features which measure visually perceivable qualities of patterns will be developed. The reason for concentrating on the SGLDM for developing such a feature set is predicated on perceptual psychology experiments and comparison studies of various texture algorithms. All of these studies indicate that second-order probabilities of the type measured by the spatial gray level dependence matrices are important in human texture discrimination and that these matrices contain more important texture-context information than the intermediate matrices of other statistical texture analysis algorithms.