Abstract
In this paper we propose an efficient normalized cross correlation (NCC) algorithm for pattern matching based on adaptive multilevel successive elimination. This successive elimination scheme is applied in conjunction with an upper bound for the cross correlation derived from Cauchy-Schwarz inequality. To apply the successive elimination, we partition the summation of cross correlation into different levels with the partition order determined by the gradient energies of the partitioned regions in the template. Thus, this adaptive multi-level successive elimination scheme can be employed to early reject most candidates to reduce the computational cost. Experimental results show the proposed algorithm is very efficient for pattern matching under different lighting conditions.
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© 2007 Springer-Verlag Berlin Heidelberg
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Wei, SD., Lai, SH. (2007). Efficient Normalized Cross Correlation Based on Adaptive Multilevel Successive Elimination. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_60
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DOI: https://doi.org/10.1007/978-3-540-76386-4_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
Online ISBN: 978-3-540-76386-4
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