Adaptive nonlinear correlation with a binary mask invariant to rotation and scale

A new adaptive methodology for pattern recognition invariant to scale, rotation and translation is presented. In order to get rotation invariance, this methodology generates an adaptive binary rings mask taking information from the 2D separable scale transform of the target image. With this mask a vectorial signature is generated getting samples from the scale transformation of the target image and it does the same for every problem image. Finally, by the use of a new adaptive nonlinear correlation, the comparison of the signatures of the target and the problem image were done. The performance of this methodology has been experimental verified with a statistical analysis, to know the mean correlation confidence level in the classification of 30 different phytoplankton images in gray scale with rotations from 0° to 180° and ±10% of maximum scale variation with respect to the target image..