Visual recognition using concurrent and layered parameter networks

A vision system to recognize 3-D objects is presented. A novel notion of generalized feature makes it possible to develop a homogeneous architecture to support recognition from simple partial features to complex feature assemblies and 3-D objects. Layered concurrent parameter transforms vote for feature hypotheses on the basis of image data and previously reconstructed features. Recognition networks, motivated by connectionist systems, collect votes, fuse evidence from various sources and ensure global consistency. In addition, they provide an integrated solution to the segmentation problem. The highly modular design allows fundamentally different types of features to interact in a coherent way, utilizing redundancies for more robust recognition. Within this paradigm, a system extracts planar patches, patches of quadrics of revolution, and the intersection curves of these surfaces (lines and conic sections in three-space) from a depth map. Reconstructed features index into a model database to form consistent object hypotheses. Experimental results detailing the recognition behavior on real depth maps are included.