Cross Correlation: Unlocking Patterns in Computer Vision
About this ebook
In signal processing, cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. This is also known as a sliding dot product or sliding inner-product. It is commonly used for searching a long signal for a shorter, known feature. It has applications in pattern recognition, single particle analysis, electron tomography, averaging, cryptanalysis, and neurophysiology. The cross-correlation is similar in nature to the convolution of two functions. In an autocorrelation, which is the cross-correlation of a signal with itself, there will always be a peak at a lag of zero, and its size will be the signal energy.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Cross-correlation
Chapter 2: Autocorrelation
Chapter 3: Covariance matrix
Chapter 4: Estimation of covariance matrices
Chapter 5: Cross-covariance
Chapter 6: Autocovariance
Chapter 7: Variational Bayesian methods
Chapter 8: Normal-gamma distribution
Chapter 9: Expectation-maximization algorithm
Chapter 10: Griffiths inequality
(II) Answering the public top questions about cross correlation.
(III) Real world examples for the usage of cross correlation in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Cross Correlation.
