Abstract :
The space requirements for indexing under perspective projections are addressed. It is known that the surface representing the set of possible images of a model point set within the index space must be three-dimensional (Jacobs, 1996). Under affine projections, the representing surface can be factorized as the cartesian product of lower-dimensional surfaces: these are obtained by projecting the representing surface onto orthogonal subspaces of the index space (Jacobs, 1992; Weinshall, 1993). This paper shows that, under perspective, such a factorization does not exist, yielding a negative answer to a question left open in (Jacobs, 1996). However, it is shown that there exist subspaces of the index space, onto which the representing surface projection is two-dimensional
Keywords :
computational geometry; computer vision; image matching; object recognition; 2D perspective images; 3D model indexing; affine projections; cartesian product; computer vision; factorization; image matching; object recognition; orthogonal subspaces; perspective projections; space requirements; surface representation; three dimensional model indexing; Artificial intelligence; Cameras; Computer vision; Image recognition; Indexing; Jacobian matrices; Layout; Orbital robotics; Runtime; Solid modeling;