Name
Abstract | ||
|---|---|---|
In this paper we show how the uncertainty within a 3d recognition process can be modeled using Bayesian nets. Reliable object
features in terms of object rims are introduced to allow a robust recognition of industrial free-form objects. Dependencies
between observed features and the objects are modeled within the Bayesian net. An algorithm to build the Bayesian net from
a set of CAD models is introduced. In the recognition, entering evidence into the Bayesian net reduces the set of possible
object hypotheses. Furthermore, the expected change of the joint probability distribution allows an integration of decision
reasoning in the Bayesian propagation. The selection of the optimal, next action is incorporated into the Bayesian nets to
reduce the uncertainty.
|
Year | DOI | Venue |
|---|---|---|
1998 | 10.1007/BFb0054779 | ECCV (2) |
Keywords | Field | DocType |
handling uncertainty,bayesian networks,object recognition,probability distribution,bayesian network | Computer vision,Variable-order Bayesian network,Joint probability distribution,Computer science,Bayesian average,Bayesian network,Artificial intelligence,Bayesian hierarchical modeling,Bayesian statistics,Machine learning,Cognitive neuroscience of visual object recognition,Bayesian probability | Conference |
ISBN | Citations | PageRank |
3-540-64613-2 | 3 | 0.46 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Björn Krebs | 1 | 20 | 4.83 |
| M. Burkhardt | 2 | 3 | 0.46 |
| bernd korn | 3 | 16 | 4.90 |