Multi-resolution image registration using multi-class Hausdorff fraction

Recently, a new image registration method, based on the Hausdorff fraction and a multi-resolution search of the transformation space, has been develop ed in the literature. This method has been applied to problems involving tr anslations, translation and scale, and affine transformations. In this pape r, we adapt the above method to the set of similarity transformations. We a lso introduce a new variant of the Hausdorff fraction similarity measure ba sed on a multi-class approach, which we call the multi-class Hausdorff frac tion (MCHF). The multi-class approach is more efficient because it matches feature points only if they are from the same class. To validate our approa ch, we segment edge maps into two classes which are the class of straight l ines and the class of curves, and we apply the new multi-class approach to two image registration examples, using synthetic and real images, respectiv ely. Experimental results show that the multi-class approach speeds up the multi-resolution search algorithm. (C) 2002 Elsevier Science B.V. All right s reserved.