Indexing to 3D model aspects using 2D contour features

We present a shape-based method of indexing to model aspects from a single intensity image. Objects are assumed to be rigid. A model aspect is represented by a 2½D edgemap and the parts of the object silhouette. Part decomposition is derived from a codon representation of the object silhouette. Invariant features extracted from each part are then used to index into a hash table to generate model-aspect hypotheses. Knowledge about parts is incorporated in voting schemes to order hypotheses for efficient verification of candidate models. Verification of model-aspect hypotheses is carried out by an alignment algorithm that is robust to partial occlusion. Results of tests using 658 model aspects from 100 objects demonstrate that accurate recognition can be achieved with very few verification attempts