Using global consistency to recognise Euclidean objects with an uncalibrated camera

A recognition strategy consisting of a mixture of indexing on invariants and search, allows objects to be recognised up to a Euclidean ambiguity with an uncalibrated camera. The approach works by using projective invariants to determine all the possible projectively equivalent models for a particular imaged object; then a system of global consistency constraints is used to determine which of these projectively equivalent, but Euclidean distinct, models corresponds to the objects viewed. These constraints follow from properties of the imaging geometry. In particular, a recognition hypothesis is equivalent to an assertion about, among other things, viewing conditions and geometric relationships between objects, and these assertions must be consistent for hypotheses to be correct. The approach is demonstrated to work on images of real scenes consisting of polygonal objects and polyhedra