@article {14890,
	title = {The space requirements of indexing under perspective projections},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {18},
	year = {1996},
	month = {1996/03//},
	pages = {330 - 333},
	abstract = {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},
	keywords = {2D, 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},
	isbn = {0162-8828},
	doi = {10.1109/34.485561},
	author = {Jacobs, David W.}
}
@conference {14837,
	title = {Space efficient 3-D model indexing},
	booktitle = {Computer Vision and Pattern Recognition, 1992. Proceedings CVPR {\textquoteright}92., 1992 IEEE Computer Society Conference on},
	year = {1992},
	month = {1992/06//},
	pages = {439 - 444},
	abstract = {It is shown that the set of 2-D images produced by a group of 3-D point features of a rigid model can be optimally represented with two lines in two high-dimensional spaces. This result is used to match images and model groups by table lookup. The table is efficiently built and accessed through analytic methods that account for the effect of sensing error. In real images, it reduces the set of potential matches by a factor of several thousand. This representation of a model{\textquoteright}s images is used to analyze two other approaches to recognition. It is determined when invariants exist in several domains, and it is shown that there is an infinite set of qualitatively similar nonaccidental properties},
	keywords = {3-D, error;table, features;sensing, indexing;3-D, lookup;, lookup;image, model, point, processing;table},
	doi = {10.1109/CVPR.1992.223153},
	author = {Jacobs, David W.}
}
