A common method for finding an object's pose is the generalized Hough transform, which accumulates evidence for possible coordinate transformations in a parameter space and takes large clusters of similar transformations as evidence of a correct solution. We analyze this approach by deriving theoretical bounds on the set of transformations consistent with each data-model feature pairing, and by deriving bounds on the likelihood of false peaks in the parameter space, as a function of noise, occlusion, and tessellation effects. We argue that blithely applying such methods to complex recognition tasks is a risky proposition, as the probability of false positives can be very high.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6039 |
| Date | 01 May 1988 |
| Creators | Grimson, W. Eric L., Huttenlocher, David |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 40 p., 5359682 bytes, 2031093 bytes, application/postscript, application/pdf |
| Relation | AIM-1044 |
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