Correlation Pattern Recognition
Cambridge University Press, Nov 24, 2005 - Computers
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.
What people are saying - Write a review
We haven't found any reviews in the usual places.
2 Mathematical background
3 Linear systems and filtering theory
4 Detection and estimation
5 Correlation filter basics
6 Advanced correlation filters
Other editions - View all
ÁÁÁ algorithm analytic signal average binary BPOF chapter circular circular convolution coherent complex component computed convolution correlation filters correlation output correlation peak correlation plane covariance covariance matrix cross-correlation CTFT ð Þ DCCF decision boundary delta function denote detection diagonal diffraction distance drive value DT signal estimate example expression ﬁlter filter design filter SLM filter value follows Fourier transform frequency domain Gaussian RVs input image input noise intensity inverse Jones calculus Jones matrix Jones vector lens linear LSI system MACH filter magnitude Mahalanobis distance mapping matrix maximal method metric modulation multiple obtained operating curve optical correlator optimal filter parameter pattern recognition phase pixel produce propagation random process ratio sampled signal samples sinusoid SLM’s spatial spectral statistically independent target image test image training images variance vector zero Þ¼