Abstract
An approach to processing images of three-dimensional objects which does not require knowledge of the ordering of the samples specifying the coordinates of points on the object surface is considered. The approach is based on obtaining a secondary analytical description of an object in the form of a polynomial function of a hypercomplex variable that projects samples of the object surface onto a sphere. It is shown that the method allows one to estimate scaling and rotation parameters and to recognize three-dimensional objects from polynomial coefficients. The performance of the proposed algorithms was estimated in experiments using images of real objects and mathematical models.
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