Rapid Object Indexing and Recognition Using Enhanced Geometric Hashing

We address the problem of 3D object recognition from a single 2D image using a model database. We develop a new method called enhanced geometric hashing. This approach allows us to solve for the indexing and the matching problem in one pass with linear complexity. Use of quasi-invariants allows us to index images in a new type of geometric hashing table. They include topological information of the observed objects inducing a high numerical stability. We also introduce a more robust Hough transform based voting method, and thus obtain a fast and robust recognition algorithm that allows us to index images by their content. The method recognizes objects in the presence of noise and partial occlusion and we show that 3D objects can be recognized from any viewpoint if only a limited number of key views are available in the model database.