Parallel matching of 3D articulated object recognition

In dealing with large volume image data, sequential methods usually are too slow and unsatisfactory. This paper introduces a new system employing para llel matching in high-level recognition of 3D articulated objects. A new st ructural strategy using linear combination. and parallel graphic matching t echniques is presented for 3D polyhedral objects representable by 2D line-d rawings. It solves one of the basic concerns in diffusion tomography comple xities, i.e. patterns can be reconstructed through fewer projections, and 3 D objects can be recognized by a few learning sample views. It also improve s some of the current methods while overcoming their drawbacks. Furthermore , it can distinguish very similar objects and is more accurate than other m ethods in the literature. An online webpage system for understanding and re cognizing 3D objects is also illustrated.