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 2005 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.
• A self-contained introduction to the field, covering prerequisite topics • Includes case studies with significant relevance • Background of authors spans academia, industry and government, giving the material breadth
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.
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