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Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation

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Abstract

The ability to quickly locate one or more instances of a model in a grey scale image is of importance to industry. The recognition/localization must be fast and accurate. In this paper we present an algorithm which incorporates normalized correlation into a pyramid image representation structure to perform fast recognition and localization. The algorithm employs an estimate of the gradient of the correlation surface to perform a steepest descent search. Test results are given detailing search time by target size, effect of rotation and scale changes on performance, and accuracy of the subpixel localization algorithm used in the algorithm. Finally, results are given for searches on real images with perspective distortion and the addition of Gaussian noise.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada, M5S 1A1

    W. James MacLean

  2. Department of Computer Science, York University, Toronto, Canada, M3J 1P3

    John K. Tsotsos

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  1. W. James MacLean
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  2. John K. Tsotsos
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Correspondence to W. James MacLean.

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MacLean, W.J., Tsotsos, J.K. Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation. Machine Vision and Applications 19, 163–179 (2008). https://doi.org/10.1007/s00138-007-0089-8

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  • DOI: https://doi.org/10.1007/s00138-007-0089-8

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