Computer Methods For Analysis And Synthesis Of Visual Texture

Computer Methods for Analysis and Synthesis of Visual Texture

Computer Methods for Analysis and Synthesis of Visual Texture

Computer methods are presented which deal with the problems of visual texture, specifically Texture Analysis and Texture Synthesis. The Decision Theory Method, as its name indicates, is based on the principles of statistical decision theory. This method in conjunction with the Internal Covering Theory, automatically generates a set of interval complexes which act as 2-D filters that detect texture features in the scene of analysis. The versatility of this method to deal with various problems of visual texture, such as scene segmentation using textural information, extraction of texture borders, discrimination/recognition of both spatially and chromatically textured scenes, etc. is demonstrated. A model is proposed that views the pixels of a digitized textural scene as a two- way seasonal time series. Based on this model, a method is developed for synthesis of natural looking textures. This method possesses a desirable quality that the parameters needed for the synthesis are derived from the analysis of parent'' texture, i.e., the texture to be imitated. Extending the concepts of two-dimensional formal grammars, known as array grammars, multilevel array grammars'' are introduced. These are shown capable of generating complex texture scenes, specifically brickwall-type'' texture, that is, those textures which are perceived at many levels and at each level, they can be either structural or statistical. A way of specifying these grammars for texture scenes in real cases, with the help of interval complexes, '' is also indicated. (96 references) (auth).

Visual Texture

This book surveys the state of the art in multidimensional, physically-correct visual texture modeling. Features: reviews the entire process of texture synthesis, including material appearance representation, measurement, analysis, compression, modeling, editing, visualization, and perceptual evaluation; explains the derivation of the most common representations of visual texture, discussing their properties, advantages, and limitations; describes a range of techniques for the measurement of visual texture, including BRDF, SVBRDF, BTF and BSSRDF; investigates the visualization of textural information, from texture mapping and mip-mapping to illumination- and view-dependent data interpolation; examines techniques for perceptual validation and analysis, covering both standard pixel-wise similarity measures and also methods of visual psychophysics; reviews the applications of visual textures, from visual scene analysis in medical applications, to high-quality visualizations in the automotive industry.