Overview andGoals Pattern recognition has evolved as a mature field of data analysis and its practice involves decision making using a wide variety of machine learning tools. Over the last three decades, substantial advances have been made in the areas of classification, prediction, optimisation and planning algorithms. Inparticular, the advances made in the areas of non-linear classification, statistical pattern recognition, multi-objective optimisation, string matching and uncertainty management are notable. These advances have been triggered by the availability of cheap computing power which allows large quantities of data to be processed in a very short period of time, and therefore developed algorithms can be tested easily on real problems. The current focus of pattern recognition research and development is to take laboratory solutions to commercial applications. The main goal of this book is to provide researchers with some of the latest novel techniques in the area of pattern recognition, and to show the potential of such techniques on real problems. The book will provide an excellent background to pattern recognition students and researchers into latest algorithms for pattern matching, and classification and their practical applications for imaging and non-imaging applications. Organization and Features The book is organised in two parts. The first nine chapters of the book describe novel advances in the areas of graph matching, information fusion, data clustering and classification, feature extraction and decision making under uncertainty.
The field of pattern recognition has emerged as one of the most challenging and important endeavours in the area of information technology research. Research in the area of pattern recognition has benefits for improving many areas of human endeavour, including medicine, the economy, the environment, and security. This book presents some of the latest advances in the area of pattern recognition theory and applications.
The first half of the book discusses novel pattern classification and matching schemes, and the second half describes the application of novel tools in biometrics and digital multimedia. The applications included, such as face/iris recognition, handwriting recognition, surveillance, human dynamics, sensor fusion, etc., provide a detailed insight into how to build real pattern recognition systems and how to evaluate them. Given the dynamic nature of technology evolution in this area, this book provides the latest algorithms and concepts that can be used to build real systems.
Features and topics:
Provides state-of-the art algorithms, as well as presents cutting-edge applications within the field
Introduces achievements in theoretical pattern recognition, including statistical and Bayesian pattern recognition, structural pattern recognition, neural networks, classification and data mining, evolutionary approaches to optimisation, and knowledge based systems
Offers insights and support to practitioners concerned with the state-of-the art technology in the area
Progress in Pattern Recognition addresses the needs of postgraduate students, researchers, and practitioners in the areas of computer science, engineering and mathematics where pattern recognition techniques are widely used.
Professor Sameer Singh is Director of the Research School of Informatics, Loughborough University, UK, and serves as Editor-in-Chief of the Springer journal, Pattern Analysis and Applications
