1-D fast normalized cross-correlation using additions

Authors:
Jae-Chern Yoo;Byoung Deog Choi;Hyoung-Kee Choi
Affiliations:
School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do, 440-746, Republic of Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do, 440-746, Republic of Korea;School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Gyeonggi-Do, 440-746, Republic of Korea
Venue:
Digital Signal Processing
Year:
2010

Citing 7
Cited 0

Digital image processing (2nd ed.)

Digital image processing (2nd ed.)
Field Programmable Gate Array Based Radar Front-End Digital Signal Processing

FCCM '99 Proceedings of the Seventh Annual IEEE Symposium on Field-Programmable Custom Computing Machines
Fast normalized cross correlation for defect detection

Pattern Recognition Letters
A Fast Template Matching Algorithm with Adaptive Skipping Using Inner-Subtemplates' Distances

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
FPGA Implementation of Embedded Cruise Control and Anti-Collision Radar

DSD '06 Proceedings of the 9th EUROMICRO Conference on Digital System Design
Correlation search in graph databases

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Template Matching Based on Normalized Cross Correlation With Adaptive Multilevel Winner Update

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new fast algorithm for the computation of the normalized cross-correlation (NCC) is presented. For a search window of size M and a template of size N, our fast NCC requires only approximately 3N@?(M-N+1) additions/subtractions without multiplications. Numerical results with 100,000 test signals show that the use of the fast NCC instead of the traditional approaches for the determination of the degree of similarity between a test signal and a reference signal (template) brings about a significant improvement in terms of false negative rate, identification rate and computational cost without a significant increase in false positive rate, especially when signal to noise ratio (SNR) is higher than 4 dB.