Asymmetric correlation: A noise robust similarity measure for template matching

Elhanan Elboher, Michael Werman

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


We present an efficient and noise robust template matching method based on asymmetric correlation (ASC). The ASC similarity function is invariant to affine illumination changes and robust to extreme noise. It correlates the given non-normalized template with a normalized version of each image window in the frequency domain. We show that this asymmetric normalization is more robust to noise than other cross correlation variants, such as the correlation coefficient. Direct computation of ASC is very slow, as a DFT needs to be calculated for each image window independently. To make the template matching efficient, we develop a much faster algorithm, which carries out a prediction step in linear time and then computes DFTs for only a few promising candidate windows. We extend the proposed template matching scheme to deal with partial occlusion and spatially varying light change. Experimental results demonstrate the robustness of the proposed ASC similarity measure compared to state-of-the-art template matching methods.

Original languageAmerican English
Article number6497607
Pages (from-to)3062-3073
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number8
StatePublished - 2013


  • Asymmetric correlation
  • cross correlation
  • noise robust similarity
  • phase correlation
  • template matching


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