A study on SSD calculation between input image and subpixel-translated template images and its applications to a subpixel image matching problem

摘要

Pattern matching between input and template images, which is carried out using Sum of Squared Differences (SSD), a similarity value, has been widely used in various computer vision applications such as stereo measurements and super-resolution image syntheses. The crucial process in the pattern matching problem is estimating the translation of the input image to match both images; a technique exists for improving the accuracy of the translation estimation at the subpixel level. In addition, subpixel estimation accuracy is improved by synthetic template images that are assumed to represent subpixel translated images using linear interpolation. However, calculation cost increases because the technique necessitates additional SSD calculations for the synthetic template images. To eliminate the need for additional SSD calculations, we found that we can obtain additional SSD values for the synthetic subpixel translated images by calculating just the SSD values for the original template images: we never need additional SSD calculations. Moreover, based on this knowledge, we proposed a novel algorithm for speeding up the estimation error cancellation (EEC) method that was developed for estimating subpixel displacements in pattern matching. Experimental results demonstrated the advantages of the proposed algorithm.