- published: 23 Jul 2016
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In signal processing, cross-correlation is a measure of similarity of two series as a function of the lag 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.
For continuous functions f and g, the cross-correlation is defined as:
where denotes the complex conjugate of
and
is the lag.
Similarly, for discrete functions, the cross-correlation is defined as:
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 power.
In probability and statistics, the term cross-correlations is used for referring to the correlations between the entries of two random vectors X and Y, while the autocorrelations of a random vector X are considered to be the correlations between the entries of X itself, those forming the correlation matrix (matrix of correlations) of X. This is analogous to the distinction between autocovariance of a random vector and cross-covariance of two random vectors. One more distinction to point out is that in probability and statistics the definition of correlation always includes a standardising factor in such a way that correlations have values between −1 and +1.
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Noise location with cross-correlation
This video explains cross correlation technique
Correlation provides a measure of similarity between two signals. This video explains process of correlating discrete signals and highlights when normalised correlation is required.
This video illustrates the concepts of auto and cross correlation and their applications in time delay (lag) measurements
This video is part of the Udacity course "Computational Photography". Watch the full course at https://www.udacity.com/course/ud955
In this video we have teach the concept of cross correlation and auto correlation with solve example and if you want the notes please type your email id on comment box Notes: DFT https://drive.google.com/open?id=0B_I5V9NmraH6a1YxVkl6cU5Za00 if you have any doubt you can put in comment box you can even contact us at facebook: www.facebook.com/lastmomenttuition www.facebook.com/sumerr3 and even you can call us contact no. are in Notes
In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. 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. For continuous functions f and g, the cross-correlation is defined as: This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
This video is part of the Udacity course "Computational Photography". Watch the full course at https://www.udacity.com/course/ud955
Noise location with distance measurement
This video explains cross correlation technique
Correlation provides a measure of similarity between two signals. This video explains process of correlating discrete signals and highlights when normalised correlation is required.
This video illustrates the concepts of auto and cross correlation and their applications in time delay (lag) measurements
This video is part of the Udacity course "Computational Photography". Watch the full course at https://www.udacity.com/course/ud955
In this video we have teach the concept of cross correlation and auto correlation with solve example and if you want the notes please type your email id on comment box Notes: DFT https://drive.google.com/open?id=0B_I5V9NmraH6a1YxVkl6cU5Za00 if you have any doubt you can put in comment box you can even contact us at facebook: www.facebook.com/lastmomenttuition www.facebook.com/sumerr3 and even you can call us contact no. are in Notes
In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. 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. For continuous functions f and g, the cross-correlation is defined as: This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
This video is part of the Udacity course "Computational Photography". Watch the full course at https://www.udacity.com/course/ud955
Noise location with distance measurement
Video Lecture Series by IIT professors (Not Available in NPTEL) Video Lectures on "Signals and Systems" by Prof. S.C. Dutta Roy Sir For more Video Lectures...... www.satishkashyap.com For free ebooks ...... www.ebook29.blogspot.com 1. Introduction to the Course and Basic Concepts 2. Signals & their Transportation 3. Elementary Signals in the Discrete Time Domain 4. Characterisation of Signals 5. Basic concepts of Linear Time Systems 6. Convolution Invertibility, & Stability Causality 7. Stability Unit, Step Response and Differential Equations 8. Systems Described by Differential & Difference Equations 9. Fourier & His Series 10. More About Fourier Series (With Uncomfortable Questions) 11. Those Uncomfortable Questions about the Existence of Fourier & Series and Some More 12. Introducti...
In this tutorial, I discuss the concept of cross-correlation and how it can be used to study and analyze images obtained from a PIV set-up.
Analog Integrated Circuit Design, Professor Ali Hajimiri California Institute of Technology (Caltech) http://chic.caltech.edu/hajimiri/ © Copyright, Ali Hajimiri
Photogrammetry I Course, Chapter: Matching - Part A (Cross Correlation) This lecture is part of the Photogrammetry I course at BSc level taught by Cyrill Stachniss at the University of Bonn, Germany in the summer term 2015. Slide Information * The slides have been created by Cyrill Stachniss as part of the photogrammetry and/or robotics courses. * I tried to acknowledge all people who contributed images or videos. In case I made a mistake or missed someone, please let me know. * The photogrammetry material heavily relies on the very well written lecture notes by Wolfgang Förstner and the Photogrammetric Computer Vision book by Förstner & Wrobl. * Parts of the robotics material, espcially the algorithms, stems from the great Probabilistic Robotics book by Thrun, Burgard and Fox. * Feel f...
example 03: registering an image using normalized cross-correlation
RES.LL-005 D4M: Signal Processing on Databases, Fall 2012 View the complete course: http://ocw.mit.edu/RESLL-005F12 Instructor: Jeremy Kepner Genectic sequence analysis using associative arrays. Creating of a genetic processing pipeline. Ingest of genetic sample data into a database. Sub-sampling of data. Correlation of genetic samples using associative array multiplication. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
ECSE-4540 Intro to Digital Image Processing Rich Radke, Rensselaer Polytechnic Institute Lecture 14: Object and feature detection (3/30/15) 0:00:02 Object detection 0:01:24 Template matching 0:02:46 Cross-correlation 0:09:54 Matlab cross-correlation examples 0:20:55 Image features 0:22:28 What makes a good feature? 0:27:00 Shi-Tomasi corner detector 0:32:49 Matlab corner detection 0:43:11 The scale of a feature 0:45:34 Affine invariance 0:47:32 More advanced features 0:49:48 SIFT features 0:54:37 SIFT matching examples 1:01:16 Features in visual effects Follows Section 12.1-12.2 of the textbook (Gonzalez and Woods, 3rd ed.). For more details on feature detection and description, see my other videos: https://www.youtube.com/watch?v=9-F5-gUIAWE https://www.youtube.com/watc...
Topic: Correlation receiver structure For more infomation, contact Professor Alexander M. Wyglinski (alexw@wpi.edu).