An Interactive Approach to Solving Correspondence Problems

Finding correspondences among objects in different
images is a critical problem in computer vision. Even
good correspondence procedures can fail, however, when
faced with deformations, occlusions, and differences in lighting
and zoom levels across images. We present a methodology
for augmenting correspondence matching algorithms
with a means for triaging the focus of attention and effort
in assisting the automated matching. For guiding the mix of
human and automated initiatives, we introduce a measure of
the expected value of resolving correspondence uncertainties.
We explore the value of the approach with experiments
on benchmark data.