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Image matching is an important area of research in the field of artificial intelligence, machine vision and visual navigation. A new image matching scheme in which grey scale images are quantised to form sub-band binary images is presented. The information in the binary images is then signaturised and the signatures are sorted as per significance. These sorted signatures are then normalised to transform the represented image pictorial features in the form of a hyper-dimensional vector cluster. For the image matching, the two clusters from both the images are compared in the transformed domain. This comparison yields efficient results directly in the image spatial domain avoiding the need of image inverse transformation for the interpretation of results. As compared with the conventional techniques, this comparison avoids the wide range of square error calculations all over the image. It also directly guides the solution in an iterative fashion to converge towards the true match point. The process of signaturisation is based on image local features and is moulded in a way to support the scale and rotation-invariant template matching as well. A four-dimensional solution population scheme has also been presented with an associated matching confidence factor. This factor helps in terminating the iterations when the essential matching conditions have been achieved. The proposed scheme gives robust and fast results for normal, scaled and rotated templates. Speed comparison with older techniques shows the computational viability of this new technique and its much lesser dependence on image size. The method also shows noise immunity at 30 dB additive white Gaussian noise and impulsive noise.