This book is an introduction to pattern theory, the theory behind the task of analyzing types of signals that the real world presents to us. It deals with generating mathematical models of the patterns in those signals and algorithms for analyzing the data based on these models. It exemplifies the view of applied mathematics as starting with a collection of problems from some area of science and then seeking the appropriate mathematics for clarifying the experimental data and the underlying processes of producing these data. An emphasis is placed on finding the mathematical and, where needed, computational tools needed to reach those goals, actively involving the reader in this process. Among other examples and problems, the following areas are treated: music as a realvalued function of continuous time, character recognition, the decomposition of an image into regions with distinct colors and textures, facial recognition, and scaling effects present in natural images caused by their statistical selfsimilarity.

PrefaceNotationWhat Is Pattern Theory?English Text and Markov ChainsMusic and Piece wise Gaussian ModelsCharacter Recognition and Syntactic GroupingImage Texture, Segmentation and Gibbs ModelsFaces and Flexible TemplatesNatural Scenes and their Multiscale AnalysisBibliographyIndex