DocumentCode
3207014
Title
Task-specific utility in a general Bayes net vision system
Author
Rimey, Raymond D. ; Brown, Christopher M.
Author_Institution
Dept. of Comput. Sci., Rochester Univ., NY, USA
fYear
1992
fDate
15-18 Jun 1992
Firstpage
142
Lastpage
147
Abstract
TEA is a task-oriented computer vision system that uses Bayes nets and a maximum expected-utility decision rule to choose a sequence of task-dependent and opportunistic visual operations on the basis of their cost and (present and future) benefit. The authors discuss technical problems regarding utilities, present TEA-1´s utility function (which approximates a two-step lookahead), and compare it to various simpler utility functions in experiments with real and simulated scenes
Keywords
Bayes methods; computer vision; inference mechanisms; probabilistic logic; Bayes nets; maximum expected-utility decision rule; opportunistic visual operations; simpler utility functions; task-oriented computer vision system; two-step lookahead; utility function; Application software; Cameras; Computational modeling; Computer science; Computer vision; Costs; Decision theory; Decision trees; Layout; Machine vision;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Society Conference on
Conference_Location
Champaign, IL
ISSN
1063-6919
Print_ISBN
0-8186-2855-3
Type
conf
DOI
10.1109/CVPR.1992.223214
Filename
223214
Link To Document