Julian Eggert, Chen Zhang, Edgar Körner, "Template matching for large transformations", Artificial Neural Networks, 17. International Conference (ICANN), pp. 169-179, 2007.

Finding a template image in another larger image is a problem that has applications in many vision research areas such as models for object detection and tracking. The main problem here is that under real-world conditions the searched image usually is a deformed version of the template, so that these deformations have to be taken into account by the matching procedure. A common way to do this is by minimizing the difference between the template and patches of the search image assuming that the template can undergo 2D affine transformations. A popular differential algorithm for achieving this has been proposed by Lucas and Kanade [1], with the disadvantage that it works only for small transformations. Here we investigate the transformation properties of a differential template matching approach by using resolution pyramids in combination with transformation pyramids, and show how we can do template matching under large-scale transformations, with simulation results indicating that the scale and rotation ranges can be doubled using a 3 stage pyramid.