Abstract
A novel approach based on the generation and combination of geometric and topologic evidences has been developed to identify and extract semantic features from a part solid model representation. The proposed method exploits Bayesian probabilistic propagation. It works by constructing and partitioning a graph which combines the original cavity graph representing the depression with a set of virtual links. The methods for updating these beliefs are in accordance with Bayesian probability rules.
Original language | English (US) |
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Pages | 75-83 |
Number of pages | 9 |
State | Published - 1994 |
Event | Proceedings of the 1994 International Mechanical Engineering Congress and Exposition - Chicago, IL, USA Duration: Nov 6 1994 → Nov 11 1994 |
Other
Other | Proceedings of the 1994 International Mechanical Engineering Congress and Exposition |
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City | Chicago, IL, USA |
Period | 11/6/94 → 11/11/94 |
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Mechanical Engineering