The author deals with high level visualization, understanding, and interpretation of three-dimensional (3-D) polyhedral objects from two-dimensional (2-D) line-drawing images for document analysis. A new scheme is presented, which is aimed at learning, representing and recognizing 3-D objects with only very few learning samples. It can strengthen advantages of some current key methods while overcome their drawbacks. Further, it will be able to distinguish objects with very similar patterns and is more accurate than other existing methods. Several illustrations are given. Future research topics are discussed.
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