A number of papers in the Computer Vision and Pattern Recognition
literature have demonstrated that invariants, or equivalently
structure modulo a 3D linear transformation, are sufficient for object
recognition. The final stage in the recognition process is
verification, where an outline is transferred from an acquisition
image of the object to the target image.
For the most part recognition based on invariants has concentrated on
planar objects, though some 3D invariants have been measured from
single and multiple images for polyhedra, point sets, surfaces of
revolution and algebraic surfaces. The work so far on surfaces of
revolution has only exploited isolated points on the outline (such as
bitangents), and has not addressed transfer or verification.
This thesis, for the first time, extends the transfer and extraction
of invariants to surfaces of revolution using the entire outline.
Given a single view of the surface, it is possible to obtain the
projection in any other given view, given a minimal number of points
in the target image. In particular it is is possible to reconstruct
the generating curve, and thereby a rich set of invariants.
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