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English
Cambridge University Press
10 June 2010
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.

By:   , ,
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Dimensions:   Height: 244mm,  Width: 170mm,  Spine: 21mm
Weight:   640g
ISBN:   9780521153485
ISBN 10:   0521153484
Pages:   404
Publication Date:  
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
Preface; 1. Introduction; 2. Mathematical background; 3. Linear systems and filtering theory; 4. Detection and estimation; 5. Correlation filter basics; 6. Advanced correlation filters; 7. Optical considerations; 8. Limited-modulation filters; 9. Applications of correlation filters; References; Index.

Reviews for Correlation Pattern Recognition

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


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