Dw. Jacobs, THE SPACE REQUIREMENTS OF INDEXING UNDER PERSPECTIVE PROJECTIONS, IEEE transactions on pattern analysis and machine intelligence, 18(3), 1996, pp. 330-333
Object recognition systems can be made more efficient through the use
of table lookup to match features. The cost of this indexing process d
epends on the space required to represent groups of model features in
such a lookup table. We determine the space required to perform indexi
ng of arbitrary sets of 3-D model points for lookup from a single 2-D
image formed under perspective projection. We show that in this case.
one must use a 3-D surface to represent model groups, and we provide a
n analytic description of such a surface. This is in contrast to the c
ases of scaled-orthographic or affine projection, in which only a 2-D
surface is required to represent a group of model features [3], [10].
This demonstrates a fundamental way in which the recognition of object
s under perspective projection is more complex than is recognition und
er other projection models.