Volume 113, Issue 6, June 2009, Pages 726-742

We present a method for matching feature points robustly across widely separated images. In general, it
is difficult to match feature points correctly by using only the similarity between local descriptors. In our
approach, the correspondence problem is formulated as an optimization problem with one-to-one correspondence
constraints. A novel objective function is defined to preserve local image-to-image affine
transformations across correspondences. This objective function enables our method to cope with significant
viewpoint or scale changes between images, unlike previous methods that relied on the assumption
that the distance or orientation between neighboring feature points are preserved across images. A relaxation
algorithm is proposed for maximizing the objective function, which imposes one-to-one correspondence
constraints, unlike conventional relaxation labeling algorithms that impose many-to-one
correspondence constraints. Experimental evaluation shows that our method is robust with respect to
significant viewpoint changes, scale changes, and nonrigid deformations between images, in the presence
of repeated textures that make feature point matching more ambiguous. Our method is also applied to
object recognition in cluttered environments, giving some promising results.