Consensus-based Cross-correlation

摘要

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.