Automatic area based registration method and its application to the surface inspection of steel industry products

We describe the automated application of an area based registration method to the surface inspection of steel industry products as a tool to solve an intermediate mosaicing problem. The main problem of area based methods is that there is high probability that the results of a matching process will be incorrect if a region of interest without any relevant detail is used. The selection of a region of interest with relevant content continues to be a problem nowadays. We propose a method to select a salient area when using a zero mean normalised cross correlation metric and a block as a region of interest. The selection of the size and the position of the block is focused on ensuring a smooth unimodal similarity surface around the maximum similitude point. Experiments show a correlation between the surface kurtosis of the block autocovariance and the same coefficient measured over the correlation surface around the maximum similitude point for the three different steel products analysed. We check that the maximum correlation value is reached abruptly, in a small range of pixels around the maximum similitude point, in correlation surfaces obtained from blocks containing non-relevant information. On the other hand, salient blocks usually lead to unimodal smooth similarity surfaces with small sensitivity to noise in contrast with the ones obtained from non-remarkable blocks. Also, the method proposed allows the application of fast search algorithms based on the unimodality of the correlation surface, obtaining high computational time reduction in comparison with full search strategies using fast normalised cross correlation algorithms.