Sparse-pixel recognition of primitives in engineering drawings
Identifieur interne : 003153 ( Istex/Corpus ); précédent : 003152; suivant : 003154Sparse-pixel recognition of primitives in engineering drawings
Auteurs : Dov Dori ; Yubin Liang ; Joseph Dowell ; Ian ChaiSource :
- Machine Vision and Applications [ 0932-8092 ] ; 1993-03-01.
Abstract
Abstract: Recognition of primitives in technical drawings is the first stage in their higher level interpretation. It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. The sparse-pixel-based algorithms yield high quality recognition, as demonstrated on a sample of engineering drawings.
Url:
DOI: 10.1007/BF01211932
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ISTEX:1E6F2EC58A7EE2BE93EDA02356FE60BC82D2E24DLe document en format XML
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It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. The sparse-pixel-based algorithms yield high quality recognition, as demonstrated on a sample of engineering drawings.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Dov Dori</name><affiliations><json:string>Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, 32000, Haifa, Israel</json:string></affiliations></json:item><json:item><name>Yubin Liang</name><affiliations><json:string>Department of Computer Science, University of Kansas, 66045, Lawrence, KS, USA</json:string></affiliations></json:item><json:item><name>Joseph Dowell</name><affiliations><json:string>Department of Computer Science, University of Kansas, 66045, Lawrence, KS, USA</json:string></affiliations></json:item><json:item><name>Ian Chai</name><affiliations><json:string>Department of Computer Science, University of Kansas, 66045, Lawrence, KS, USA</json:string></affiliations></json:item></author><articleId><json:string>BF01211932</json:string><json:string>Art2</json:string></articleId><language><json:string>eng</json:string></language><abstract>Abstract: Recognition of primitives in technical drawings is the first stage in their higher level interpretation. It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. 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It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. The sparse-pixel-based algorithms yield high quality recognition, as demonstrated on a sample of engineering drawings.</p></abstract><textClass><keywords scheme="Journal Subject"><list><head>Computer Science</head><item><term>Image Processing</term></item><item><term>Communications Engineering, Networks</term></item><item><term>Pattern Recognition</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1993-03-01">Published</change><change xml:id="refBibs-istex" who="#ISTEX-API" when="2016-3-20">References added</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/1E6F2EC58A7EE2BE93EDA02356FE60BC82D2E24D/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Springer, Publisher found" wicri:toSee="no header"><istex:xmlDeclaration>version="1.0" encoding="UTF-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//Springer-Verlag//DTD A++ V2.4//EN" URI="http://devel.springer.de/A++/V2.4/DTD/A++V2.4.dtd" name="istex:docType"></istex:docType><istex:document><Publisher><PublisherInfo><PublisherName>Springer-Verlag</PublisherName><PublisherLocation>Berlin/Heidelberg</PublisherLocation></PublisherInfo><Journal><JournalInfo JournalProductType="ArchiveJournal" NumberingStyle="Unnumbered"><JournalID>138</JournalID><JournalPrintISSN>0932-8092</JournalPrintISSN><JournalElectronicISSN>1432-1769</JournalElectronicISSN><JournalTitle>Machine Vision and Applications</JournalTitle><JournalSubTitle>An International Journal</JournalSubTitle><JournalAbbreviatedTitle>Machine Vis. 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It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. The sparse-pixel-based algorithms yield high quality recognition, as demonstrated on a sample of engineering drawings.</Para></Abstract><KeywordGroup Language="En"><Heading>Key words</Heading><Keyword>Engineering drawing understanding</Keyword><Keyword>Document recognition</Keyword><Keyword>Computer-Aided Design (CAD)</Keyword><Keyword>CAD conversion</Keyword><Keyword>Raster-to-vector</Keyword><Keyword>Vectorization</Keyword></KeywordGroup></ArticleHeader><NoBody></NoBody></Article></Issue></Volume></Journal></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Sparse-pixel recognition of primitives in engineering drawings</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Sparse-pixel recognition of primitives in engineering drawings</title></titleInfo><name type="personal" displayLabel="corresp"><namePart type="given">Dov</namePart><namePart type="family">Dori</namePart><affiliation>Faculty of Industrial Engineering and Management, Technion, Israel Institute of Technology, 32000, Haifa, Israel</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yubin</namePart><namePart type="family">Liang</namePart><affiliation>Department of Computer Science, University of Kansas, 66045, Lawrence, KS, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Joseph</namePart><namePart type="family">Dowell</namePart><affiliation>Department of Computer Science, University of Kansas, 66045, Lawrence, KS, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ian</namePart><namePart type="family">Chai</namePart><affiliation>Department of Computer Science, University of Kansas, 66045, Lawrence, KS, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer-Verlag</publisher><place><placeTerm type="text">Berlin/Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">1993-03-01</dateIssued><copyrightDate encoding="w3cdtf">1993</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: Recognition of primitives in technical drawings is the first stage in their higher level interpretation. It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. 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