Efficient Geodesic Attribute Thinnings Based on the Barycentric Diameter
An attribute opening is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a—more general— attribute thinning . In this paper, we propose efficient grey...
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Veröffentlicht in: | Journal of mathematical imaging and vision 2013-05, Vol.46 (1), p.128-142 |
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creator | Morard, Vincent Decencière, Etienne Dokládal, Petr |
description | An
attribute opening
is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a—more general—
attribute thinning
. In this paper, we propose efficient grey scale thinnings based on geodesic attributes.
Given that the geodesic diameter is time consuming, we propose a new geodesic attribute, the
barycentric diameter
to speed up the computation time. Then, we give the theoretical error bound between these two attributes, and we note that in practice, the barycentric diameter gives very similar results in comparison with the geodesic diameter. Finally, we present the algorithm with further optimisations, to obtain a 60× speed up.
We illustrate the use of these thinnings in automated non-destructive material inspection: the detection of cracks. We discuss the advantages of these operators over other methods such as path openings or the supremum of openings with segments. |
doi_str_mv | 10.1007/s10851-012-0374-7 |
format | Article |
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attribute opening
is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a—more general—
attribute thinning
. In this paper, we propose efficient grey scale thinnings based on geodesic attributes.
Given that the geodesic diameter is time consuming, we propose a new geodesic attribute, the
barycentric diameter
to speed up the computation time. Then, we give the theoretical error bound between these two attributes, and we note that in practice, the barycentric diameter gives very similar results in comparison with the geodesic diameter. Finally, we present the algorithm with further optimisations, to obtain a 60× speed up.
We illustrate the use of these thinnings in automated non-destructive material inspection: the detection of cracks. We discuss the advantages of these operators over other methods such as path openings or the supremum of openings with segments.</description><identifier>ISSN: 0924-9907</identifier><identifier>EISSN: 1573-7683</identifier><identifier>DOI: 10.1007/s10851-012-0374-7</identifier><language>eng</language><publisher>Boston: Springer US</publisher><subject>Applications of Mathematics ; Applied sciences ; Artificial intelligence ; Computer Science ; Computer science; control theory; systems ; domain_math.tr-img ; Exact sciences and technology ; Image Processing ; Image Processing and Computer Vision ; Information retrieval. Graph ; Mathematical Methods in Physics ; Mathematics ; Pattern recognition. Digital image processing. Computational geometry ; Signal,Image and Speech Processing ; Theoretical computing</subject><ispartof>Journal of mathematical imaging and vision, 2013-05, Vol.46 (1), p.128-142</ispartof><rights>Springer Science+Business Media, LLC 2012</rights><rights>2014 INIST-CNRS</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-60b76e6740d1d62ec8ca9d9358a167a03f79cf40670e44cd6393ba0ccf614a63</citedby><cites>FETCH-LOGICAL-c395t-60b76e6740d1d62ec8ca9d9358a167a03f79cf40670e44cd6393ba0ccf614a63</cites><orcidid>0000-0002-6502-7461 ; 0000-0002-1349-8042</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10851-012-0374-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10851-012-0374-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,314,777,781,882,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27618497$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://minesparis-psl.hal.science/hal-00834415$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Morard, Vincent</creatorcontrib><creatorcontrib>Decencière, Etienne</creatorcontrib><creatorcontrib>Dokládal, Petr</creatorcontrib><title>Efficient Geodesic Attribute Thinnings Based on the Barycentric Diameter</title><title>Journal of mathematical imaging and vision</title><addtitle>J Math Imaging Vis</addtitle><description>An
attribute opening
is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a—more general—
attribute thinning
. In this paper, we propose efficient grey scale thinnings based on geodesic attributes.
Given that the geodesic diameter is time consuming, we propose a new geodesic attribute, the
barycentric diameter
to speed up the computation time. Then, we give the theoretical error bound between these two attributes, and we note that in practice, the barycentric diameter gives very similar results in comparison with the geodesic diameter. Finally, we present the algorithm with further optimisations, to obtain a 60× speed up.
We illustrate the use of these thinnings in automated non-destructive material inspection: the detection of cracks. We discuss the advantages of these operators over other methods such as path openings or the supremum of openings with segments.</description><subject>Applications of Mathematics</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer Science</subject><subject>Computer science; control theory; systems</subject><subject>domain_math.tr-img</subject><subject>Exact sciences and technology</subject><subject>Image Processing</subject><subject>Image Processing and Computer Vision</subject><subject>Information retrieval. Graph</subject><subject>Mathematical Methods in Physics</subject><subject>Mathematics</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Signal,Image and Speech Processing</subject><subject>Theoretical computing</subject><issn>0924-9907</issn><issn>1573-7683</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp9kMFOAyEQhonRxFp9AG978eABHRYWlmOttWvSxEvvhLLQ0rRsA1sT3142a3r0RIb5vsnMj9AjgRcCIF4TgboiGEiJgQqGxRWakEpQLHhNr9EEZMmwlCBu0V1KewCoSyImqFk45423oS-Wtmtt8qaY9X30m3Nvi_XOh-DDNhVvOtm26ELR72wu4o_JSszwu9dH29t4j26cPiT78PdO0fpjsZ43ePW1_JzPVthQWfWYw0ZwywWDlrS8tKY2WraSVrUmXGigTkjjGHABljHTcirpRoMxjhOmOZ2i53HsTh_UKfpjXkV12qtmtlLDXz6MMkaqb5JZMrImdilF6y4CATWkpsbUVE5NDakpkZ2n0TnpZPTBRR2MTxexFJzUTA5cOXIpt8LWRrXvzjHky_8Z_gsGaXtN</recordid><startdate>20130501</startdate><enddate>20130501</enddate><creator>Morard, Vincent</creator><creator>Decencière, Etienne</creator><creator>Dokládal, Petr</creator><general>Springer US</general><general>Springer</general><general>Springer Verlag</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-6502-7461</orcidid><orcidid>https://orcid.org/0000-0002-1349-8042</orcidid></search><sort><creationdate>20130501</creationdate><title>Efficient Geodesic Attribute Thinnings Based on the Barycentric Diameter</title><author>Morard, Vincent ; Decencière, Etienne ; Dokládal, Petr</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-60b76e6740d1d62ec8ca9d9358a167a03f79cf40670e44cd6393ba0ccf614a63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Applications of Mathematics</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer Science</topic><topic>Computer science; control theory; systems</topic><topic>domain_math.tr-img</topic><topic>Exact sciences and technology</topic><topic>Image Processing</topic><topic>Image Processing and Computer Vision</topic><topic>Information retrieval. Graph</topic><topic>Mathematical Methods in Physics</topic><topic>Mathematics</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Signal,Image and Speech Processing</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Morard, Vincent</creatorcontrib><creatorcontrib>Decencière, Etienne</creatorcontrib><creatorcontrib>Dokládal, Petr</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of mathematical imaging and vision</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Morard, Vincent</au><au>Decencière, Etienne</au><au>Dokládal, Petr</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Efficient Geodesic Attribute Thinnings Based on the Barycentric Diameter</atitle><jtitle>Journal of mathematical imaging and vision</jtitle><stitle>J Math Imaging Vis</stitle><date>2013-05-01</date><risdate>2013</risdate><volume>46</volume><issue>1</issue><spage>128</spage><epage>142</epage><pages>128-142</pages><issn>0924-9907</issn><eissn>1573-7683</eissn><abstract>An
attribute opening
is an idempotent, anti-extensive and increasing operator, which removes from an image connected components which do not fulfil a given criterion. When the increasingness property is dropped, we obtain a—more general—
attribute thinning
. In this paper, we propose efficient grey scale thinnings based on geodesic attributes.
Given that the geodesic diameter is time consuming, we propose a new geodesic attribute, the
barycentric diameter
to speed up the computation time. Then, we give the theoretical error bound between these two attributes, and we note that in practice, the barycentric diameter gives very similar results in comparison with the geodesic diameter. Finally, we present the algorithm with further optimisations, to obtain a 60× speed up.
We illustrate the use of these thinnings in automated non-destructive material inspection: the detection of cracks. We discuss the advantages of these operators over other methods such as path openings or the supremum of openings with segments.</abstract><cop>Boston</cop><pub>Springer US</pub><doi>10.1007/s10851-012-0374-7</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-6502-7461</orcidid><orcidid>https://orcid.org/0000-0002-1349-8042</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Applications of Mathematics Applied sciences Artificial intelligence Computer Science Computer science control theory systems domain_math.tr-img Exact sciences and technology Image Processing Image Processing and Computer Vision Information retrieval. Graph Mathematical Methods in Physics Mathematics Pattern recognition. Digital image processing. Computational geometry Signal,Image and Speech Processing Theoretical computing |
title | Efficient Geodesic Attribute Thinnings Based on the Barycentric Diameter |
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