Object Recognition Using Local Geometric Constraints: A Robust Alternative To Tree-Search

A new algorithm is presented for recognising 3D polyhedral objects in a 2D segmented image using local geometric constraints between 2D line segments. Results demonstrate the success of the algorithm at coping with poorly segmented images that would cause substantial problems for many current algorithms. The algorithm adapts to use with either 3D line data or 2D polygonal objects; either case increases its efficiency. The conventional approach of searching an interpretation tree and pruning it using local constraints is discarded; the new approach accumulates the information available from the local constraints and forms match hypotheses subject to two global constraints that are enforced using the competitive paradigm. All stages of processing consist of many extremely simple and intrinsically parallel operations. This parallelism means that the algorithm is potentially very fast, and contributes to its robustness. It also means that the computation can be guaranteed to complete after a known time.