Efficient two dimensional object recognition

The problem of recognition of multiple flat objects in a cluttered environment from an arbitrary viewpoint (weak perspective) is addressed. The models are acquired automatically and initially approximated by polygons with multiple line tolerances for robustness. Groups of consecutive segments (supersegments) are then gray-coded and entered into a hash table. This provides the essential mechanism for indexing and fast retrieval. Once the database of all models is built, the recognition proceeds by segmenting the scene into a polygonal approximation; the gray code for each supersegment retrieves model hypotheses from the hash table. Hypotheses are clustered if they are mutually consistent and represent the instance of a model. The estimate of the transformation is refined. This methodology makes it possible to recognize models in the presence of noise, occlusion, scale, rotation, translation, and weak perspective. Unlike most of the current systems, its complexity grows as O(kN), where N is the number of models and k<<1/.<<ETX>>