Abstract
We formulate structure from motion as a Bayesian inference problem, and use a Markov chain Monte Carlo sampler to sample the posterior on this problem. This results in a method that can identify both small and large tracker errors, and yields reconstructions that are stable in the presence of these errors. Furthermore, the method gives detailed information on the range of ambiguities in structure given a particular dataset, and requires no special geometric formulation to cope with degenerate situations. Motion segmentation is obtained by a layer of discrete variables associating a point with an object. We demonstrate a sampler that successfully samples an approximation to the marginal on this domain, producing a relatively unambiguous segmentation.
Original language | English (US) |
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Pages | 660-665 |
Number of pages | 6 |
State | Published - 1999 |
Externally published | Yes |
Event | Proceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99) - Kerkyra, Greece Duration: Sep 20 1999 → Sep 27 1999 |
Other
Other | Proceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99) |
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City | Kerkyra, Greece |
Period | 9/20/99 → 9/27/99 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition