enforced by the present method the advantage of this approach is that it applies to both the continuous and discontinuous parts of the signal transformation the block diagram of fig1 illustrates the applications of the method described below data samples 1 and 2 are taken from a data source 10 the data source 10 may be for example a visible scene in which case the samples 1 and 2 are images of that scene the data sample domain 11 is a subset of the data source 10 the samples 1 and 2 are subject to data processing 12 which yields a transformation model 13 the transformation model 13 can be used to provide an image recognition system 14 for detecting previously unanalysed data sample 3 alternatively the transformation model 13 may be used in a prediction system 15 to create a novel data sample corresponding to an artificially generated view of the data source 10 fig2 a to 2 f illustrate the general approach to image matching fig2 a and 2 b show images f u2032 and f u2033 representing different views of the same scene the images are matched by identifying points with similar colours and the resulting spatial flow is shown in fig2 c note that the flow is smooth two further views are shown in fig2 d and 2 e which can be matched as before however in this example the resulting spatial flow is not smooth although signal matching algorithms are known for matching common features between two images the method described herein increases the accuracy and the completeness of the computed mappings the accuracy can be improved because the dimensionality constraint described below can be used to make.