  Change in viewpoint is one of the major factors for variation in object
appearance across different images. Thus, view-invariant object recognition is
a challenging and important image understanding task. In this paper, we propose
a method that can match objects in images taken under different viewpoints.
Unlike most methods in the literature, no restriction on camera orientations or
internal camera parameters are imposed and no prior knowledge of 3D structure
of the object is required. We prove that when two cameras take pictures of the
same object from two different viewing angels, the relationship between every
quadruple of points reduces to the special case of homography with two equal
eigenvalues. Based on this property, we formulate the problem as an error
function that indicates how likely two sets of 2D points are projections of the
same set of 3D points under two different cameras. Comprehensive set of
experiments were conducted to prove the robustness of the method to noise, and
evaluate its performance on real-world applications, such as face and object
recognition.
