Symmetry based semantic analysis of engineering drawings

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Publication Type pre-print
School or College College of Engineering
Department Computing, School of
Creator Henderson, Thomas C.
Other Author Boonsirisumpun, Narong; Joshi, Anshul
Title Symmetry based semantic analysis of engineering drawings
Date 2014-01-01
Description Engineering drawings have posed significant challenges to image analysis for many decades. The goal is to take images of scanned engineering drawings and interpret them so as to understand their contents (e.g., characters, digits, line segments, box segments etc.). This is known as semantic analysis. We propose a new approach here which takes advantage of the man-made nature of drawings: there is a tremendous amount of symmetry. We exploit this insight to enhance our previously reported system, the Non-Deterministic Agent System (NDAS), with symmetry-based analysis tools. Agents work independently but use each others results to produce the final result (e.g., form segmentation, character analysis, structural analysis, boundary segmentation, etc.). We use the wreath product representation both to characterize symmetry as well as to structure a Bayesian network model of the uncertainty. This approach permits wide application to perform semantic analysis of engineering drawings.
Type Text
Publisher Institute of Electrical and Electronics Engineers (IEEE)
First Page 6997698
Language eng
Bibliographic Citation Henderson, T. C., Boonsirisumpun, N., & Joshi, A. (2014). Symmetry based semantic analysis of engineering drawings. Processing of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014, 6997698.
Rights Management (c) 2014 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Format Medium application/pdf
Format Extent 601,332 bytes
Identifier uspace,19273
ARK ark:/87278/s6kq1b9k
Setname ir_uspace
Date Created 2015-02-13
Date Modified 2021-05-06
ID 712815
Reference URL https://collections.lib.utah.edu/ark:/87278/s6kq1b9k
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