An Affine Invariant Eulidean Distance Between Images

In this paper, we propose a novel affine invariant Euclidean distance of images, named annulus Euclidean distance (AED). Unlike the traditional Euclidean distance, the AED takes into account the spatial correlation between images. Therefore, it is invariant to the scale, rotation and shift transformation for images. The method is motivated that the annulus is intuitively rotation invariant, then combined with the centralization and normalization of the images, the AED is variant to affine transformation (AT). The key advantage of this distance is that it is a simple and tractable way to measure the difference between images with some deformations. Some examples in optical character recognition (OCR) and texture retrieval are presented to show the power of the AED. Experimental results demonstrate that the AED is a simple, efficient and powerful way to measure the difference between images