Abstract: 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 use 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. The features in the image are modeled as points in the plane, and the structure in the image is a planar graph between the features. By modeling the object in the image as a planar graph we allow the approximations to be designed with point- set matching algorithms, geometric invariants, and graph- processing algorithms. The sieve algorithm is demonstrated on three problems. The first is registering images of muscles taken with an electron microscope. The second is aligning images of geometric patterns taken with a charged- couple device (CCD) camera. The third is recognizing objects taken with a CCD camera. !15
展开▼
