Symbolic representation and distributed matching strategies for schematics Takatsuka, Masahiro, Caelli, Terry M., West, Geoff A. W. and Venkatesh, Svetha 1999, Symbolic representation and distributed matching strategies for schematics, in ICDAR 1999 : Proceedings of the 5th International Conference on Document Analysis and Recognition, IEEE, [Washington, D. C.], pp. 693-696, doi: 10.1109/ICDAR.1999.791882. Attached Files Name Description MIMEType Size Downloads venkatesh-symbolicrepresentation-1999.pdf Published version application/pdf 199.87KB 31 Title Symbolic representation and distributed matching strategies for schematics Author(s) Takatsuka, Masahiro Caelli, Terry M. West, Geoff A. W. Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Document Analysis and Recognition. Conference (5th : 1999 : Bangalore, India) Conference location Bangalore, India Conference dates 20-22 Sep. 1999 Title of proceedings ICDAR 1999 : Proceedings of the 5th International Conference on Document Analysis and Recognition Editor(s) [Unknown] Publication date 1999 Conference series Document Analysis and Recognition. Conference Start page 693 End page 696 Total pages 4 Publisher IEEE Place of publication [Washington, D. C.] Keyword(s) distributed algorithms engineering drawings hopfield neural networks neural networks object recognition Summary This paper describes object-centered symbolic representation and distributed matching strategies of 3D objects in a schematic form which occur in engineering drawings and maps. The object-centered representation has a hierarchical structure and is constructed from symbolic representations of schematics. With this representation, two independent schematics representing the same object can be matched. We also consider matching strategies using distributed algorithms. The object recognition is carried out with two matching methods: (1) matching between an object model and observed data at the lowest level of the hierarchy, and (2) constraints propagation. The first is carried out with symbolic Hopfield-type neural networks and the second is achieved via hierarchical winner-takes-all algorithms Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ISBN 0769503187 Language eng DOI 10.1109/ICDAR.1999.791882 Field of Research 089999 Information and Computing Sciences not elsewhere classified Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1.1 Full written paper - refereed Copyright notice ©1999, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30044820 Document type: Conference Paper Collections: School of Information Technology Open Access Collection Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Tue, 01 May 2012, 11:00:21 EST Fri, 04 May 2012, 09:49:07 EST Fri, 04 May 2012, 09:51:53 EST Mon, 07 May 2012, 13:45:25 EST Tue, 06 Sep 2016, 18:51:47 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 227 Abstract Views, 31 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 11:00:21 EST

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