An efficient registration and recognition algorithm via sieve processes

A fundamental problem in computer vision is establishing correspondence between features in two images of the same scene. The computational burden in this problem is solving for the optimal mapping and transformation between the two scenes. In this paper we present a sieve algorithm for efficiently estimating the transformation and correspondence. A sieve algorithm uses approximations to generate a sequence of increasingly accurate estimates of the correspondence. Initially, the approximations are computationally inexpensive and are designed to quickly sieve through the space of possible solutions. As the space of possible solutions shrinks, greater accuracy is required and the complexity of the approximations increases.