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Correlation Pattern Recognition (Paperback)
  • Correlation Pattern Recognition (Paperback)
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Correlation Pattern Recognition (Paperback)

(author), (author), (author)
£57.99
Paperback 404 Pages / Published: 10/06/2010
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Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing. This book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition. Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.

Publisher: Cambridge University Press
ISBN: 9780521153485
Number of pages: 404
Weight: 640 g
Dimensions: 244 x 170 x 21 mm


MEDIA REVIEWS
Review of the hardback: ' ... well-written with many diagrams and gray-scale images to illustrate the concepts ... would be especially useful for pattern recognition practitioners interested in expanding their tool dchest b eyond basic correlation.' IAPR Newsletter
"An excellent book...It provides an overview of the discipline and summarizes recent research that should facilitate the seasoned worker in this field. Highly recommended." Choice
"This book educates the reader on the richness of correlation...This book is well-written with many diagrams and gray-scale images to illustrate the concepts...This book is intended for advanced undergraduate and graduate students, and would be especially useful for pattern recognition practitioners interested in expanding their tool chest beyond basic correlation." IAPR Newsletter

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