"Inference of a 3-D Object From a Partial 2-D Projection"
Proceedings of the ACM/SIGAPP Symposium on Applied Computing,
Kansas City, MO, March 1-3, pp. 417-425, 1992.
by Hsu, P.P., and E. Triantaphyllou
Abstract:
The problem we examine here is how to infer the entire topology of an
unknown 3-D object from a random partial 2 -D projection. In our research we
combine the model graph [Wong and Fu, 1985] with the AHR graph to create what
we call, the Adjacency Graph (or AG graph). The AG graph exhibits many interesting
properties. These properties relate the way the nodes and branches are connected in
complete and incomplete AG graphs. A complete AG graph describes the entire topology
of a 3-D object while an incomplete AG graph describes a partial topology of that object.
Therefore, the problem we solve here is how to infer a complete AG graph from an
incomplete AG graph. The proposed approached is demonstrated by an example taken from
the literature. Furthermore, this approach is very efficient on both time and space
requirements.
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