Paper Info
Title
Consensus-based cross-correlation
Abstract
Cross-correlation is a classical similarity measure with broad applications in multimedia signal processing. While it is robust against uncorrelated noise in the input signals, it is severely affected by systematic disturbances which lead to biased results. To overcome this limitation, we propose in this paper consensus-based cross-correlation (ConCor) to deal with heavily corrupted signal parts that derail regular cross-correlation. ConCor builds upon the widely adopted RANSAC algorithm to reliably identify and eliminate corrupt signal parts at limited additional complexity. Our approach is universal in that it can be combined with existing cross-correlation variants. We apply ConCor in two example applications, namely video synchronization and template matching. Our experimental results demonstrate the improved robustness and accuracy when compared to classical cross-correlation.
Year
DOI
Venue
2011
10.1145/2072298.2071996
ACM Multimedia 2001
Keywords
Field
DocType
consensus-based cross-correlation,ransac algorithm,classical similarity measure,regular cross-correlation,corrupted signal part,input signal,cross-correlation variant,corrupt signal part,classical cross-correlation,broad application,paper consensus-based cross-correlation,signal processing,synchronization,cross correlation,template matching
Template matching,Cross-correlation,Data mining,Synchronization,Similarity measure,Pattern recognition,RANSAC,Computer science,Uncorrelated,Robustness (computer science),Multimedia signal processing,Artificial intelligence
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
5
Name
Order
Citations
PageRank
Florian Schweiger1505.03
Georg Schroth225012.71
Michael Eichhorn3133.51
Eckehard G. Steinbach42221299.71
Michael Fahrmair512414.12