Bibliographic Citation
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Title | Evidential knowledge-based computer vision |
Creator/Author | Wesley, L.P. |
Publication Date | 1986 Mar 01 |
OSTI Identifier | OSTI ID: 5569184 |
Other Number(s) | CODEN: OPEGA |
Resource Type | Journal Article |
Resource Relation | Opt. Eng. ; Vol/Issue: 25:3 |
Research Org | SRI International, Artificial Intelligence Center, Menlo Park, CA 94025 |
Subject | 990200 -- Mathematics & Computers; EXPERT SYSTEMS-- RESEARCH PROGRAMS;SUPERCOMPUTERS-- ARTIFICIAL INTELLIGENCE;SUPERCOMPUTERS-- IMAGE PROCESSING; DATA COVARIANCES;PROGRAMMING;STATISTICS |
Related Subject | COMPUTERS;DIGITAL COMPUTERS;MATHEMATICS;PROCESSING |
Description/Abstract | It has been argued that knowledge-based systems (KBSs) must reason from evidential information, i.e., from information that is to some degree uncertain, imprecise, and occasionally inaccurate.^ This in no less true of KBSs that operate in the domain of computer-based image interpretation.^ Recent research has suggested that the work of Dempster and Shafer (DS) provides a viable alternative to Bayesian-based techniques for reasoning from evidential information.^ In this paper, the authors discuss some differences between the DS theory and some popular Bayesian-based approaches to effecting the reasoning task.^ They then discuss some work on integrating the DS theory into a knowledge-based high-level computer vision system in order to examine various aspects of this new technology that have not been explored to data.^ Results from a large number of image interpretation experiments are presented.^ These results suggest that a KBS`s performance improves substantially when it exploits various features of the DS theory that are not readily available in pure Bayesian-based approaches. |
Country of Publication | United States |
Language | English |
Format | Pages: 363-379 |
System Entry Date | 2001 May 13 |
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