An integrated approach to 2D object recognition

摘要

A multilevel Markov Random Field (MRF) energy environment has been developed that simultaneously performs delineation, representation and classification of two-dimensional objects by using a global optimization technique. This environment supports a multipolar shape representation which establishes a dynamic MRF structure. This structure is initialized as a single-center polar representation, and uses minimum description length tests to determine whether to establish new polar centers. The polar representations at these centers are compared with a database of such representations in order to identify pieces of objects, and the results of these comparisons are used to compile evidence for global object identifications. This method is potentially more robust than conventional multistaged approaches to object recognition because it incorporates all the information about the objects into a single adaptive decision process, and its use of a multipolar representation allows it to handle partially occluded objects.