We present a novel method for graph based image representation and matching. The image is represented as a graph of affine invariant regions. A local affine invariant coordinate system is used to describe the geometry at the location of each graph vertex. Once an image is encoded as a graph, a graph matching process can be initiated against the graph of any other image. Each pair of matched features induces an affine approximation of the deformation. The similarity of the affine approximations between different matchings is the foundation of a graph matching algorithm presented here, as it greatly reduces the ambiguity in matching. The algorithm is much faster than state of the art feature matching methods and produces similar results in the presence of highly complex scenes that contain background clutter, occlusion, viewpoint changes, and elastic geometric deformations.

This paper considers the problem of registering two observations of the same object, where the observations differ due to a combined effect of an affine geometric transformation and nonuniform illumination changes. The problem of deriving new representations of the observations that are both invariant to geometric transformations and linear in the illumination model is analyzed. In this framework, we present a novel method for linear estimation of illumination changes in an affine invariant manner, thus, decoupling the original problem into two simpler ones. The computational complexity of the method is low as it requires no more than solving a linear set of equations. The prior step of illumination estimation is shown to improve the accuracy of state-of-the-art registration techniques by a factor of two.

We present a novel image transform called scale manipulation (SMT). The transform can be used for object pose estimation and registration of affine transformed images in the presence of non homogenous illumination changes. The transform calculates affine invariant features of objects in a global manner and avoids using any sort of edge detection. The computational load of the method is relatively low since it is linear in the data size. In this paper we introduce the transform and demonstrate its applications for pose estimation in the presence of non uniform varying illumination.