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Consensus-based cross-correlation

  • Technical University of Munich
  • DOCOMO Euro-Labs

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publicationMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
Pages1289-1292
Number of pages4
DOIs
StatePublished - 2011
Event19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11 - Scottsdale, AZ, United States
Duration: 28 Nov 20111 Dec 2011

Publication series

NameMM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops

Conference

Conference19th ACM International Conference on Multimedia ACM Multimedia 2011, MM'11
Country/TerritoryUnited States
CityScottsdale, AZ
Period28/11/111/12/11

Keywords

  • Robust correlation
  • Synchronization
  • Template matching

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