Name
Abstract | ||
|---|---|---|
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 M × N and a template window of size m × n, the computational complexity of the traditional NCC involves 3 ċ m ċ n ċ M ċ N additions/subtractions and 2 ċ m ċ n ċ M ċ N multiplications. The required numbers of computations of the proposed sum-table scheme can be significantly reduced to only 18 ċ M ċ N additions/subtractions and 2 ċ M ċ N multiplications. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S0167-8655(03)00106-5 | Pattern Recognition Letters |
Keywords | Field | DocType |
image variance,defect detection,traditional ncc,normalized cross correlation,size m,template window,n addition,sum-table scheme,proposed sum-table scheme,sum tables,n multiplication,fast ncc computation,machine vision,cross correlation,computational complexity | Cross-correlation,Machine vision,Normalized correlation,Algorithm,Invariant (mathematics),Mathematics,Computational complexity theory,Computation | Journal |
Volume | Issue | ISSN |
24 | 15 | Pattern Recognition Letters |
Citations | PageRank | References |
58 | 2.88 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Du-Ming Tsai | 1 | 970 | 68.17 |
| Chien-Ta Lin | 2 | 87 | 4.66 |