A new approach to image registration utilising multidimensional LMS adaptive filters

A method is presented for the adaptive registration of two images between which the image object is shifted. The method is based on multidimensional least-mean-squares (LMS) adaptive filters. The filters are derived, and the performance surface is shown to be unimodal. The multidimensional filter´s similarities to and differences from the one-dimensional LMS adaptive filter are briefly discussed. It is shown that the filter will perform the necessary operations of correlation, interpolation, filtering, and shifts to produce a registered output image. An algorithm is developed to work in conjunction with the filter to provide explicit shift measurements. The advantages of this inherently parallel adaptive approach over current methods are discussed and shown to be most apparent when the images are corrupted by noise or are related by spatially varying shifts. The possibility of application of multidimensional LMS adaptive filters to pattern recognition is mentioned