Correlation filters have traditionally been designed without much
attention given to the issue of the training images within a class or
the relative spatial position between classes. We examine the
impact of training-set registration on correlation-filter performance
and develop techniques for centering the training images from a class
that result in improved performance. We also show that it is
beneficial to adjust the spatial position of the classes relative to
one another. Although the proposed techniques are relevant for many
types of correlation filter, we limit our discussion to algorithms for
the maximum average correlation height filter and the distance
classifier correlation filter.
© 2000 Optical Society of America
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