Pruning redundant skeleton branches of object in image
Skeleton is one of the most important features in image processing. In many applications such as matching, animation, tracking and so on, finding main features are important; so, obtaining target skeleton can extract suitable target features. In this paper we try to introduce a fast and accurate alg...
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creator | Azadboni, Mohammad Khodadadi Behrad, Alireza Tavakoli, Hasan |
description | Skeleton is one of the most important features in image processing. In many applications such as matching, animation, tracking and so on, finding main features are important; so, obtaining target skeleton can extract suitable target features. In this paper we try to introduce a fast and accurate algorithm to achieve main skeleton of each objects. Therefore, we suggest an appropriate approach for pulling out proper skeleton. We claim our algorithm stability is strong enough to confront edge noises. Our proposed method based on Contour Length Measure. At First, we extract object's skeleton software that we called it M-Skeleton. Second, redundant branches would be pruned by checking relevance rate for all edge pixels of target picture. So the remained branches make target's main skeleton. Most of presented skeleton algorithms are dependent on adjusting threshold, but our proposed algorithm is almost independent and experiments exhibit it can truly extract target body skeleton. |
doi_str_mv | 10.1109/IKT.2013.6620102 |
format | Conference Proceeding |
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In many applications such as matching, animation, tracking and so on, finding main features are important; so, obtaining target skeleton can extract suitable target features. In this paper we try to introduce a fast and accurate algorithm to achieve main skeleton of each objects. Therefore, we suggest an appropriate approach for pulling out proper skeleton. We claim our algorithm stability is strong enough to confront edge noises. Our proposed method based on Contour Length Measure. At First, we extract object's skeleton software that we called it M-Skeleton. Second, redundant branches would be pruned by checking relevance rate for all edge pixels of target picture. So the remained branches make target's main skeleton. Most of presented skeleton algorithms are dependent on adjusting threshold, but our proposed algorithm is almost independent and experiments exhibit it can truly extract target body skeleton.</description><identifier>EISBN: 1467364908</identifier><identifier>EISBN: 9781467364904</identifier><identifier>EISBN: 1467364894</identifier><identifier>EISBN: 9781467364898</identifier><identifier>DOI: 10.1109/IKT.2013.6620102</identifier><language>eng</language><publisher>IEEE</publisher><subject>concave angle ; convex angle ; end point ; Equations ; Image edge detection ; Length measurement ; M-Skeleton ; Mathematical model ; Noise ; Noise measurement ; Skeleton</subject><ispartof>The 5th Conference on Information and Knowledge Technology, 2013, p.411-416</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6620102$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2051,27904,54898</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6620102$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Azadboni, Mohammad Khodadadi</creatorcontrib><creatorcontrib>Behrad, Alireza</creatorcontrib><creatorcontrib>Tavakoli, Hasan</creatorcontrib><title>Pruning redundant skeleton branches of object in image</title><title>The 5th Conference on Information and Knowledge Technology</title><addtitle>IKT</addtitle><description>Skeleton is one of the most important features in image processing. In many applications such as matching, animation, tracking and so on, finding main features are important; so, obtaining target skeleton can extract suitable target features. In this paper we try to introduce a fast and accurate algorithm to achieve main skeleton of each objects. Therefore, we suggest an appropriate approach for pulling out proper skeleton. We claim our algorithm stability is strong enough to confront edge noises. Our proposed method based on Contour Length Measure. At First, we extract object's skeleton software that we called it M-Skeleton. Second, redundant branches would be pruned by checking relevance rate for all edge pixels of target picture. So the remained branches make target's main skeleton. Most of presented skeleton algorithms are dependent on adjusting threshold, but our proposed algorithm is almost independent and experiments exhibit it can truly extract target body skeleton.</description><subject>concave angle</subject><subject>convex angle</subject><subject>end point</subject><subject>Equations</subject><subject>Image edge detection</subject><subject>Length measurement</subject><subject>M-Skeleton</subject><subject>Mathematical model</subject><subject>Noise</subject><subject>Noise measurement</subject><subject>Skeleton</subject><isbn>1467364908</isbn><isbn>9781467364904</isbn><isbn>1467364894</isbn><isbn>9781467364898</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj7lOAzEURU2BFAjpI9H4B2Z43scliliiRIIi1JGX5-AQPMgzKfh7RiLVkW5xrg4hSwYtY2Af1ptdy4GJVusJwK_ILZPaCC0tdDOyGIYjADAzTQxuiH6v55LLgVaM5xJdGenwhScc-0J9dSV84kD7RHt_xDDSXGj-dge8I9fJnQZcXDgnH89Pu9Vrs317Wa8et01mRo1NRAxJGMGdkD754LALCgFE6IxEZ7T00gpmbQwdR8VDUiYaGxVXyXGhxZzc_3szIu5_6nRef_eXNPEHnhJEHg</recordid><startdate>201305</startdate><enddate>201305</enddate><creator>Azadboni, Mohammad Khodadadi</creator><creator>Behrad, Alireza</creator><creator>Tavakoli, Hasan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201305</creationdate><title>Pruning redundant skeleton branches of object in image</title><author>Azadboni, Mohammad Khodadadi ; Behrad, Alireza ; Tavakoli, Hasan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-deecf3732a34bfbcae8c5e003c874ea764b493199dc82e52cf57d79d525fa2363</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>concave angle</topic><topic>convex angle</topic><topic>end point</topic><topic>Equations</topic><topic>Image edge detection</topic><topic>Length measurement</topic><topic>M-Skeleton</topic><topic>Mathematical model</topic><topic>Noise</topic><topic>Noise measurement</topic><topic>Skeleton</topic><toplevel>online_resources</toplevel><creatorcontrib>Azadboni, Mohammad Khodadadi</creatorcontrib><creatorcontrib>Behrad, Alireza</creatorcontrib><creatorcontrib>Tavakoli, Hasan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Azadboni, Mohammad Khodadadi</au><au>Behrad, Alireza</au><au>Tavakoli, Hasan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Pruning redundant skeleton branches of object in image</atitle><btitle>The 5th Conference on Information and Knowledge Technology</btitle><stitle>IKT</stitle><date>2013-05</date><risdate>2013</risdate><spage>411</spage><epage>416</epage><pages>411-416</pages><eisbn>1467364908</eisbn><eisbn>9781467364904</eisbn><eisbn>1467364894</eisbn><eisbn>9781467364898</eisbn><abstract>Skeleton is one of the most important features in image processing. In many applications such as matching, animation, tracking and so on, finding main features are important; so, obtaining target skeleton can extract suitable target features. In this paper we try to introduce a fast and accurate algorithm to achieve main skeleton of each objects. Therefore, we suggest an appropriate approach for pulling out proper skeleton. We claim our algorithm stability is strong enough to confront edge noises. Our proposed method based on Contour Length Measure. At First, we extract object's skeleton software that we called it M-Skeleton. Second, redundant branches would be pruned by checking relevance rate for all edge pixels of target picture. So the remained branches make target's main skeleton. Most of presented skeleton algorithms are dependent on adjusting threshold, but our proposed algorithm is almost independent and experiments exhibit it can truly extract target body skeleton.</abstract><pub>IEEE</pub><doi>10.1109/IKT.2013.6620102</doi><tpages>6</tpages></addata></record> |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | concave angle convex angle end point Equations Image edge detection Length measurement M-Skeleton Mathematical model Noise Noise measurement Skeleton |
title | Pruning redundant skeleton branches of object in image |
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