Abstract
An unbiased estimator of signal variance is presented for normalizing the covariance that is widely selected as a similarity measure in vast template-matching applications. It is the variance estimator of the pure signal instead of the observed signal whose variance has been typically selected to normalize the covariance.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, J.D. (2004). SNR-Invariant Normalization of the Covariance Measure for Template Matching. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_133
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DOI: https://doi.org/10.1007/978-3-540-28633-2_133
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22817-2
Online ISBN: 978-3-540-28633-2
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