Synopsis
Revised, updated, and expanded throughout, this edition of Fundamentals of Pattern Recognition addresses the field of PR from a basic mathematical view-point - treating algebraic, topological, and categorical approaches of PR using concepts from homotopy, shape, and fibre spaces theories as well as computational topology.;Maintaining the features of the first edition, this resource: provides a new section on fibrewise topology that formalizes, for the first time, multicoloured, different gray level pictures and other complex signals consisting of base and value spaces and their interconnections; explores new artificial intelligence problems on the possible interplay between recursion theory and fibrewise image topology in connection with effectiveness and decidability problems; develops original techniques and results, such as the study of extremely irregular shapes by means of the mathematical theory of shape, finite topological spaces, and category theory's applications to PR; exhibits new figures of panoramic hierarchical classifications of all mathematical approaches to PR discussed; and presents five appendices that give quick overviews of dominant concepts in PR and related fields.;Containing open research and development problems to reinforce important methodologies, this second edition of Fundamentals of Pattern Recognition is a guide for applied mathematicians; electrical and electronics, systems, and computer engineers; computer scientists; and upper-level undergraduate and graduate students in applied mathematics and computer science courses in pattern recognition theory and artificial intelligence.
Présentation de l'éditeur
Revised, updated, and expanded throughout, this edition of Fundamentals of Pattern Recognition addresses the field of PR from a basic mathematical view-point - treating algebraic, topological, and categorical approaches of PR using concepts from homotopy, shape, and fibre spaces theories as well as computational topology.;Maintaining the features of the first edition, this resource: provides a new section on fibrewise topology that formalizes, for the first time, multicoloured, different gray level pictures and other complex signals consisting of base and value spaces and their interconnections; explores new artificial intelligence problems on the possible interplay between recursion theory and fibrewise image topology in connection with effectiveness and decidability problems; develops original techniques and results, such as the study of extremely irregular shapes by means of the mathematical theory of shape, finite topological spaces, and category theory's applications to PR; exhibits new figures of panoramic hierarchical classifications of all mathematical approaches to PR discussed; and presents five appendices that give quick overviews of dominant concepts in PR and related fields.;Containing open research and development problems to reinforce important methodologies, this second edition of Fundamentals of Pattern Recognition is a guide for applied mathematicians; electrical and electronics, systems, and computer engineers; computer scientists; and upper-level undergraduate and graduate students in applied mathematics and computer science courses in pattern recognition theory and artificial intelligence.
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