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Title Evidential knowledge-based computer vision
Creator/Author Wesley, L.P.
Publication Date1986 Mar 01
OSTI IdentifierOSTI ID: 5569184
Other Number(s)CODEN: OPEGA
Resource TypeJournal Article
Resource RelationOpt. Eng. ; Vol/Issue: 25:3
Research OrgSRI International, Artificial Intelligence Center, Menlo Park, CA 94025
Subject990200 -- Mathematics & Computers; EXPERT SYSTEMS-- RESEARCH PROGRAMS;SUPERCOMPUTERS-- ARTIFICIAL INTELLIGENCE;SUPERCOMPUTERS-- IMAGE PROCESSING; DATA COVARIANCES;PROGRAMMING;STATISTICS
Related SubjectCOMPUTERS;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 PublicationUnited States
LanguageEnglish
FormatPages: 363-379
System Entry Date2001 May 13

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