Post processing technique for correlation results based on Fourier-polar transformation

In optical correlation recognition, the post-processing of correlation output results is one of the key steps to ensure the correctness of image recognizing. Neural network technique is usually used to post-process correlation output results and good result is obtained, however, this method needs to prepare a lot of training samples of correlation peaks and noise peaks in advance. Based on the polar coordinates transformations capability of transforming the rotation in Cartesian coordinate into translation and the translation invariant property of Fourier transformation, a post-processing method of correlation results based on Fourier-polar transformation is proposed. The verification results indicate that the similarity between the Fourier-polar transformations of target and its rotated images is greater compared to the similarity between the Fourier-polar transformations of target and disturbances. So the proposed method can effectively distinguish between targets and disturbances at the post processing stage, and it can avoid collecting training samples, which makes optical correlation recognition system convenient to be applied.