DMCA
Control of Selective Perception Using Bayes Nets and Decision Theory (1993)
Cached
Download Links
- [hypatia.dcs.qmw.ac.uk]
- [ray.rimey.org]
- [ray.rimey.org]
- DBLP
Other Repositories/Bibliography
| Citations: | 116 - 2 self |
BibTeX
@MISC{Rimey93controlof,
author = {Raymond Dean Rimey},
title = {Control of Selective Perception Using Bayes Nets and Decision Theory},
year = {1993}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decision-making are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure and models for the expected performance and cost of visual operators. The TEA-1 selective vision system uses Bayes nets for representation and benefitcost analysis for control of visual and non-visual actions. It is the high-level control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA-1 demonstrates that Bayes nets and decision theoretic techniques provide a general, re-usable framework for constructi...
Keyphrases
decision theory selective perception using bayes net bayes net visual operator central issue qualitative vision module additional evidence active vision system purposive behavior benefitcost analysis tea-1 demonstrates tea-1 selective vision system non-visual action selective vision system pointable multiresolution sensor geometrical structure prior knowledge knowledge representation high-level control necessary operator selective vision re-usable framework expected performance sequential decision-making decision theoretic technique
