We show that we can optimally represent the set of 2D images produced by the point features of a rigid 3D model as two lines in two high-dimensional spaces. We then decribe a working recognition system in which we represent these spaces discretely in a hash table. We can access this table at run time to find all the groups of model features that could match a group of image features, accounting for the effects of sensing error. We also use this representation of a model's images to demonstrate significant new limitations of two other approaches to recognition: invariants, and non- accidental properties.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/5960 |
| Date | 01 February 1992 |
| Creators | Jacobs, David W. |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 23 p., 2278295 bytes, 1790124 bytes, application/postscript, application/pdf |
| Relation | AIM-1353 |
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