Fast normalized cross correlation for defect detection

Normalized cross correlation 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 normalized cross correlation computation for defect detection application. 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. Given larger images of size N M × and the neighborhood window of size n m × , the computational complexity can be significantly reduced from ) ( N M n m O ⋅ ⋅ ⋅ with the traditional normalized correlation operation to only ) ( N M O ⋅ with the proposed sum-table scheme.