Аннотация

Normalized cross correlation (NCC) has been used extensively for many machine vision applications, but the traditional normalized correlation operation does not meet speed requirements for time-critical applications. In this paper, we propose a fast NCC computation for defect detection. A sum-table scheme is utilized, which allows the calculations of image mean, image variance and cross-correlation between images to be invariant to the size of template window. For an image of size <i>M &#215; N</i> and a template window of size <i>m &#215; n</i>, the computational complexity of the traditional NCC involves 3 &#267; <i>m &#267; n &#267; M &#267; N</i> additions/subtractions and 2 &#267; <i>m &#267; n &#267; M &#267; N</i> multiplications. The required numbers of computations of the proposed sum-table scheme can be significantly reduced to only 18 &#267; <i>M &#267; N</i> additions/subtractions and 2 &#267; <i>M &#267; N</i> multiplications.

Описание

Fast normalized cross correlation for defect detection

Линки и ресурсы

тэги

сообщество

  • @daill
  • @dblp
@daill- тэги данного пользователя выделены