Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons
Identifieur interne : 000454 ( Istex/Corpus ); précédent : 000453; suivant : 000455Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons
Auteurs : Yung-Sheng ChenSource :
- Computer Vision and Image Understanding [ 1077-3142 ] ; 1997.
Abstract
The improvement of producing skeletons that preserve the significant geometric features of patterns is of great importance. One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, thehidden deletable pixel(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (CH), a reduced form of larger support (twoL-pixel vectors, which is not the form ofk×ksupport) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (CYS), where the connectivity preservation is guaranteed by the use ofCH's operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (L)s. Based on the analyzing results, 3 ≤L≤ 9 are suggested for the current algorithm ofCYS.CYSis evaluated in comparison with two small support algorithms (AFP3andCH) and one larger support algorithm (VRCT) using the same patterns. Performances are reported by the number of iterations (NI), CPU time (TC), and number of unmatched pixels (Nunmatchfor bias-reduced measure). Results show that on the measure ofTC,CYSis approximately 2 to 3 times slower than the others, while on the measure ofNI, the four algorithms have approximately identical performance. On the measure ofNunmatch,CYSis approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity ofCYSandCHis identical and is better than that ofAFP3. As a result, the better bias-reduced skeletons produced byCYSmay be purchased by a reasonable computation cost.
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DOI: 10.1006/cviu.1998.0647
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons</title><author><name sortKey="Chen, Yung Sheng" sort="Chen, Yung Sheng" uniqKey="Chen Y" first="Yung-Sheng" last="Chen">Yung-Sheng Chen</name><affiliation><mods:affiliation>Department of Electrical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan, Republic of China</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:F27688E12267BF47355F7ED7228D03D577822CB1</idno><date when="1998" year="1998">1998</date><idno type="doi">10.1006/cviu.1998.0647</idno><idno type="url">https://api.istex.fr/document/F27688E12267BF47355F7ED7228D03D577822CB1/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000454</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons</title><author><name sortKey="Chen, Yung Sheng" sort="Chen, Yung Sheng" uniqKey="Chen Y" first="Yung-Sheng" last="Chen">Yung-Sheng Chen</name><affiliation><mods:affiliation>Department of Electrical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan, Republic of China</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Computer Vision and Image Understanding</title><title level="j" type="abbrev">YCVIU</title><idno type="ISSN">1077-3142</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1997">1997</date><biblScope unit="volume">71</biblScope><biblScope unit="issue">3</biblScope><biblScope unit="page" from="294">294</biblScope><biblScope unit="page" to="311">311</biblScope></imprint><idno type="ISSN">1077-3142</idno></series><idno type="istex">F27688E12267BF47355F7ED7228D03D577822CB1</idno><idno type="DOI">10.1006/cviu.1998.0647</idno><idno type="PII">S1077-3142(98)90647-6</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">1077-3142</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The improvement of producing skeletons that preserve the significant geometric features of patterns is of great importance. One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, thehidden deletable pixel(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (CH), a reduced form of larger support (twoL-pixel vectors, which is not the form ofk×ksupport) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (CYS), where the connectivity preservation is guaranteed by the use ofCH's operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (L)s. Based on the analyzing results, 3 ≤L≤ 9 are suggested for the current algorithm ofCYS.CYSis evaluated in comparison with two small support algorithms (AFP3andCH) and one larger support algorithm (VRCT) using the same patterns. Performances are reported by the number of iterations (NI), CPU time (TC), and number of unmatched pixels (Nunmatchfor bias-reduced measure). Results show that on the measure ofTC,CYSis approximately 2 to 3 times slower than the others, while on the measure ofNI, the four algorithms have approximately identical performance. On the measure ofNunmatch,CYSis approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity ofCYSandCHis identical and is better than that ofAFP3. As a result, the better bias-reduced skeletons produced byCYSmay be purchased by a reasonable computation cost.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>Yung-Sheng Chen</name><affiliations><json:string>Department of Electrical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan, Republic of China</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>The improvement of producing skeletons that preserve the significant geometric features of patterns is of great importance. One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, thehidden deletable pixel(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (CH), a reduced form of larger support (twoL-pixel vectors, which is not the form ofk×ksupport) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (CYS), where the connectivity preservation is guaranteed by the use ofCH's operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (L)s. Based on the analyzing results, 3 ≤L≤ 9 are suggested for the current algorithm ofCYS.CYSis evaluated in comparison with two small support algorithms (AFP3andCH) and one larger support algorithm (VRCT) using the same patterns. Performances are reported by the number of iterations (NI), CPU time (TC), and number of unmatched pixels (Nunmatchfor bias-reduced measure). Results show that on the measure ofTC,CYSis approximately 2 to 3 times slower than the others, while on the measure ofNI, the four algorithms have approximately identical performance. On the measure ofNunmatch,CYSis approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity ofCYSandCHis identical and is better than that ofAFP3. 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One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, thehidden deletable pixel(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (CH), a reduced form of larger support (twoL-pixel vectors, which is not the form ofk×ksupport) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (CYS), where the connectivity preservation is guaranteed by the use ofCH's operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (L)s. Based on the analyzing results, 3 ≤L≤ 9 are suggested for the current algorithm ofCYS.CYSis evaluated in comparison with two small support algorithms (AFP3andCH) and one larger support algorithm (VRCT) using the same patterns. Performances are reported by the number of iterations (NI), CPU time (TC), and number of unmatched pixels (Nunmatchfor bias-reduced measure). Results show that on the measure ofTC,CYSis approximately 2 to 3 times slower than the others, while on the measure ofNI, the four algorithms have approximately identical performance. On the measure ofNunmatch,CYSis approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity ofCYSandCHis identical and is better than that ofAFP3. As a result, the better bias-reduced skeletons produced byCYSmay be purchased by a reasonable computation cost.</p></abstract></profileDesc><revisionDesc><change when="1997-07-03">Registration</change><change when="1997">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/F27688E12267BF47355F7ED7228D03D577822CB1/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla" xml:lang="en"><item-info><jid>YCVIU</jid><aid>90647</aid><ce:pii>S1077-3142(98)90647-6</ce:pii><ce:doi>10.1006/cviu.1998.0647</ce:doi><ce:copyright type="full-transfer" year="1998">Academic Press</ce:copyright></item-info><head><ce:dochead><ce:textfn>Regular Article</ce:textfn></ce:dochead><ce:title>Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons</ce:title><ce:author-group><ce:author><ce:given-name>Yung-Sheng</ce:given-name><ce:surname>Chen</ce:surname></ce:author><ce:affiliation><ce:textfn>Department of Electrical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan, Republic of China<ce:footnote id="F1"><ce:label>f1</ce:label><ce:note-para>E-mail: eeyschen@ee.yzu.edu.tw</ce:note-para></ce:footnote><ce:cross-ref refid="F1"><ce:sup>f1</ce:sup></ce:cross-ref></ce:textfn></ce:affiliation></ce:author-group><ce:date-received day="21" month="11" year="1995"></ce:date-received><ce:date-accepted day="3" month="7" year="1997"></ce:date-accepted><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>The improvement of producing skeletons that preserve the significant geometric features of patterns is of great importance. One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, the<ce:italic>hidden deletable pixel</ce:italic>(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (<ce:bold>CH</ce:bold>), a reduced form of larger support (two<ce:italic>L</ce:italic>-pixel vectors, which is not the form of<ce:italic>k</ce:italic>×<ce:italic>k</ce:italic>support) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (<ce:bold>CYS</ce:bold>), where the connectivity preservation is guaranteed by the use of<ce:bold>CH</ce:bold>'s operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (<ce:bold>L</ce:bold>)s. Based on the analyzing results, 3 ≤<ce:italic>L</ce:italic>≤ 9 are suggested for the current algorithm of<ce:bold>CYS</ce:bold>.<ce:bold>CYS</ce:bold>is evaluated in comparison with two small support algorithms (<ce:bold>AFP3</ce:bold>and<ce:bold>CH</ce:bold>) and one larger support algorithm (<ce:bold>VRCT</ce:bold>) using the same patterns. Performances are reported by the number of iterations (<ce:italic>NI</ce:italic>), CPU time (<ce:italic>TC</ce:italic>), and number of unmatched pixels (<ce:italic>N</ce:italic><ce:inf><ce:italic>unmatch</ce:italic></ce:inf>for bias-reduced measure). Results show that on the measure of<ce:italic>TC</ce:italic>,<ce:bold>CYS</ce:bold>is approximately 2 to 3 times slower than the others, while on the measure of<ce:italic>NI</ce:italic>, the four algorithms have approximately identical performance. On the measure of<ce:italic>N</ce:italic><ce:inf><ce:italic>unmatch</ce:italic></ce:inf>,<ce:bold>CYS</ce:bold>is approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity of<ce:bold>CYS</ce:bold>and<ce:bold>CH</ce:bold>is identical and is better than that of<ce:bold>AFP3</ce:bold>. As a result, the better bias-reduced skeletons produced by<ce:bold>CYS</ce:bold>may be purchased by a reasonable computation cost.</ce:simple-para></ce:abstract-sec></ce:abstract></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Hidden Deletable Pixel Detection Using Vector Analysis in Parallel Thinning to Obtain Bias-Reduced Skeletons</title></titleInfo><name type="personal"><namePart type="given">Yung-Sheng</namePart><namePart type="family">Chen</namePart><affiliation>Department of Electrical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li 320, Taiwan, Republic of China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1997</dateIssued><dateValid encoding="w3cdtf">1997-07-03</dateValid><copyrightDate encoding="w3cdtf">1998</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">The improvement of producing skeletons that preserve the significant geometric features of patterns is of great importance. One of feasible approaches is to develop a method embedded in a known parallel algorithm to produce bias-reduced skeletons since a bias skeleton usually degrades the preservation of significant geometric features of patterns. From our observations, bias skeletons always appear in the junction of lines which form an angle less than or near equal to 90°. In this paper, thehidden deletable pixel(HDP) which influences the speed of deleting the boundary pixels on a concave side is newly defined. Based on the comparable performance of our pseudo 1-subcycle parallel thinning algorithm (CH), a reduced form of larger support (twoL-pixel vectors, which is not the form ofk×ksupport) operated by an intermediate vector analysis about the deleted pixels in each thinning iteration is developed for HDP detection to obtain bias-reduced skeletons, which can be purchased by a reasonable computation cost. HDP restoration and parallel implementation are further considered to formulate an improved algorithm (CYS), where the connectivity preservation is guaranteed by the use ofCH's operators and HDP restoration. A set of synthetic images are used to quantify the skeleton from the geometry viewpoint and investigate the skeleton variations of using different (L)s. Based on the analyzing results, 3 ≤L≤ 9 are suggested for the current algorithm ofCYS.CYSis evaluated in comparison with two small support algorithms (AFP3andCH) and one larger support algorithm (VRCT) using the same patterns. Performances are reported by the number of iterations (NI), CPU time (TC), and number of unmatched pixels (Nunmatchfor bias-reduced measure). Results show that on the measure ofTC,CYSis approximately 2 to 3 times slower than the others, while on the measure ofNI, the four algorithms have approximately identical performance. On the measure ofNunmatch,CYSis approximately 2 to 3 times less than the others. One-pixel boundary noise is also considered for exploring the noise immunity. The results suggest that the noise immunity ofCYSandCHis identical and is better than that ofAFP3. As a result, the better bias-reduced skeletons produced byCYSmay be purchased by a reasonable computation cost.</abstract><note type="content">Section title: Regular Article</note><relatedItem type="host"><titleInfo><title>Computer Vision and Image Understanding</title></titleInfo><titleInfo type="abbreviated"><title>YCVIU</title></titleInfo><genre type="Journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199809</dateIssued></originInfo><identifier type="ISSN">1077-3142</identifier><identifier type="PII">S1077-3142(00)X0031-8</identifier><part><date>199809</date><detail type="volume"><number>71</number><caption>vol.</caption></detail><detail type="issue"><number>3</number><caption>no.</caption></detail><extent unit="issue pages"><start>255</start><end>449</end></extent><extent unit="pages"><start>294</start><end>311</end></extent></part></relatedItem><identifier type="istex">F27688E12267BF47355F7ED7228D03D577822CB1</identifier><identifier type="DOI">10.1006/cviu.1998.0647</identifier><identifier type="PII">S1077-3142(98)90647-6</identifier><accessCondition type="use and reproduction" contentType="">© 1998Academic Press</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Academic Press, ©1998</recordOrigin></recordInfo></mods></metadata><enrichments><istex:catWosTEI uri="https://api.istex.fr/document/F27688E12267BF47355F7ED7228D03D577822CB1/enrichments/catWos"><teiHeader><profileDesc><textClass><classCode scheme="WOS">COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE</classCode><classCode scheme="WOS">COMPUTER SCIENCE, SOFTWARE ENGINEERING</classCode><classCode scheme="WOS">ENGINEERING, ELECTRICAL & ELECTRONIC</classCode></textClass></profileDesc></teiHeader></istex:catWosTEI></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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