Efficient Normalized Cross Correlation Based on Adaptive Multilevel Successive Elimination View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007-01-01

AUTHORS

Shou-Der Wei , Shang-Hong Lai

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. More... »

PAGES

638-646

Book

TITLE

Computer Vision – ACCV 2007

ISBN

978-3-540-76385-7
978-3-540-76386-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-76386-4_60

DOI

http://dx.doi.org/10.1007/978-3-540-76386-4_60

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020730240


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