Automatic construction of a relational model for recognition of a 3-D object
Shujun Zhang, Geoffrey D. Sullivan, Keith D. Baker · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
This paper presents a view-independent relational model (VIRM) for use in a vision system designed for recognizing known 3D objects from single monochromatic images within unknown environments. The aim if to establish a model of an object suitable for its recognition automatically without invoking pose information. To generate the VIRM, the system projects a wireframe model of the object from a number of different viewpoints, and performs a statistical inference to select relatively view-independent relationships among component parts of the object. These relations are stored as a relational model of the object represented as a hypergraph associated with procedural constraints. Three-dimensional component parts (model features) of the object, which can be associated with extended image features defined by simple 2D geometrical attributes, are used as nodes of the hypergraph. Co- visibility of model features is represented by arcs of the hypergraph. Other pairwise view- independent relations are used as procedural constraints associated with arcs of the hypergraph.