A fuzzy sets theoretic approach to approximate spatial reasoning

Li, Y; Li, S

A fuzzy sets theoretic approach to approximate spatial reasoning

Li, Y Li, S

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2004, 12 (6), pp. 745 - 754
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Li, Y	en_US
dc.contributor.author	Li, S https://orcid.org/0000-0002-3332-2546	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2004, 12 (6), pp. 745 - 754	en_US
dc.identifier.issn	1063-6706	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9051
dc.description.abstract	Relational composition-based reasoning has become the most prevalent method for qualitative reasoning since Allen's 1983 work on temporal intervals. Underlying this reasoning technique is the concept of a jointly exhaustive and pairwise disjoint set of relations. Systems of relations such as RCC5 and RCC8 were originally developed for ideal regions, not subject to imperfections such as vagueness or fuzziness which are found in many applications in geographic analysis and image understanding. This paper, however, presents a general method for classifying binary topological relations involving fuzzy regions using the RCC5 or the RCC8 theory. Our approach is based on fuzzy set theory and the theory of consonant random set. Some complete classifications of topological relations between fuzzy regions are also given. Furthermore, two composition operators on spatial relations between fuzzy regions are introduced in this paper. These composition operators provide reasonable relational composition-based reasoning engine for spatial reasoning involving fuzzy regions. © 2004 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems	en_US
dc.relation.isbasedon	10.1109/TFUZZ.2004.836100	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A fuzzy sets theoretic approach to approximate spatial reasoning	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	12	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	12	en_US

Abstract:

Relational composition-based reasoning has become the most prevalent method for qualitative reasoning since Allen's 1983 work on temporal intervals. Underlying this reasoning technique is the concept of a jointly exhaustive and pairwise disjoint set of relations. Systems of relations such as RCC5 and RCC8 were originally developed for ideal regions, not subject to imperfections such as vagueness or fuzziness which are found in many applications in geographic analysis and image understanding. This paper, however, presents a general method for classifying binary topological relations involving fuzzy regions using the RCC5 or the RCC8 theory. Our approach is based on fuzzy set theory and the theory of consonant random set. Some complete classifications of topological relations between fuzzy regions are also given. Furthermore, two composition operators on spatial relations between fuzzy regions are introduced in this paper. These composition operators provide reasonable relational composition-based reasoning engine for spatial reasoning involving fuzzy regions. © 2004 IEEE.

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http://hdl.handle.net/10453/9051