The space requirements of indexing under perspective projections

TitleThe space requirements of indexing under perspective projections
Publication TypeJournal Articles
Year of Publication1996
AuthorsJacobs DW
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination330 - 333
Date Published1996/03//
ISBN Number0162-8828
Keywords2D, complexity;feature, complexity;table, extraction;image, hashing;indexing, image, images;3D, lookup;, lookup;computational, matching;geometric, matching;indexing;object, model, points;feature, process;invariants;object, processing;table, projections;space, recognition;perspective, recognition;stereo

Object recognition systems can be made more efficient through the use of table lookup to match features. The cost of this indexing process depends on the space required to represent groups of model features in such a lookup table. We determine the space required to perform indexing of arbitrary sets of 3D model points for lookup from a single 2D image formed under perspective projection. We show that in this case, one must use a 3D surface to represent model groups, and we provide an analytic description of such a surface. This is in contrast to the cases of scaled-orthographic or affine projection, in which only a 2D surface is required to represent a group of model features. This demonstrates a fundamental way in which the recognition of objects under perspective projection is more complex than is recognition under other projection models