Detecting image pairs with a common field of view is
an important prerequisite for many computer vision tasks.
Typically, common local features are used as a criterion
for identifying such image pairs. This approach, however,
requires a reliable method for matching features, which is
generally a very difficult problem – especially in situations
with a wide baseline or ambiguities in the scene.
We propose two new approaches for the common field of
view problem. The first one is still based on feature matching.
Instead of requiring a very low false positive rate for
the feature matching, however, geometric constraints are
used to assess matches which may contain many false positives.
The second approach completely avoids hard matching
of features by evaluating the entropy of correspondence
probabilities.
We perform quantitative experiments on three different
hand-labeled scenes with varying difficulty. In moderately
difficult situations with a medium baseline and few a...