Grouping real functions defined on 3D surfaces
Scalar functions are widely used to support shape analysis and description. Their role is to sift the most significant shape information and to discard the irrelevant one, acting as a filter for the characteristics that will contribute to the description. Unfortunately, a single property, or functio...
Gespeichert in:
| Veröffentlicht in: | Computers & graphics 2013-10, Vol.37 (6), p.608-619 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 619 |
|---|---|
| container_issue | 6 |
| container_start_page | 608 |
| container_title | Computers & graphics |
| container_volume | 37 |
| creator | Biasotti, Silvia Spagnuolo, Michela Falcidieno, Bianca |
| description | Scalar functions are widely used to support shape analysis and description. Their role is to sift the most significant shape information and to discard the irrelevant one, acting as a filter for the characteristics that will contribute to the description. Unfortunately, a single property, or function, is not sufficient to characterize a shape and there is not a method to automatically select the functions that better describe a 3D object. Given a set of scalar functions defined on the same object, in this paper we propose a practical approach to automatically group these functions and select a subset of functions that are as much as possible independent of each other. Experiments are exhibited for several datasets to show the suitability of the method to improve and simplify shape analysis and classification issues.
[Display omitted]
•Grouping sets of scalar functions according to their behaviour over a shape, or a shape class.•Selection of functions that are as much as possible selection independent.•Use of a clustering technique to learn representative functions. |
| doi_str_mv | 10.1016/j.cag.2013.05.007 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1513465256</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0097849313000812</els_id><sourcerecordid>1513465256</sourcerecordid><originalsourceid>FETCH-LOGICAL-c393t-8b9189dffc23a9d3d049b253246348bb835c1e490e9c0ee42726a72bda355b6d3</originalsourceid><addsrcrecordid>eNqFkE9LwzAchoMoOKcfwFsvgpfW_G0SPMnUKQy86DmkyS8jo2tn0gp-ezs2POrpvTzv-8KD0DXBFcGkvttUzq4rigmrsKgwlidoRpRkpawVP0UzjLUsFdfsHF3kvMEYU1rzGaqWqR93sVsXCWxbhLFzQ-y7XHgIsQNf9F3BHos8pmAd5Et0Fmyb4eqYc_Tx_PS-eClXb8vXxcOqdEyzoVSNJkr7EBxlVnvmMdcNFYzymnHVNIoJR4BrDNphAE4lra2kjbdMiKb2bI5uD7u71H-OkAezjdlB29oO-jEbIgjjtaCi_h_lfPJAtFITSg6oS33OCYLZpbi16dsQbPYazcZMGs1eo8HCTBqnzs1x3mZn25Bs52L-LVIpqWKMTNz9gYNJy1eEZLKL0DnwMYEbjO_jHy8_V5GE8Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1448731988</pqid></control><display><type>article</type><title>Grouping real functions defined on 3D surfaces</title><source>Elsevier ScienceDirect Journals</source><creator>Biasotti, Silvia ; Spagnuolo, Michela ; Falcidieno, Bianca</creator><creatorcontrib>Biasotti, Silvia ; Spagnuolo, Michela ; Falcidieno, Bianca</creatorcontrib><description>Scalar functions are widely used to support shape analysis and description. Their role is to sift the most significant shape information and to discard the irrelevant one, acting as a filter for the characteristics that will contribute to the description. Unfortunately, a single property, or function, is not sufficient to characterize a shape and there is not a method to automatically select the functions that better describe a 3D object. Given a set of scalar functions defined on the same object, in this paper we propose a practical approach to automatically group these functions and select a subset of functions that are as much as possible independent of each other. Experiments are exhibited for several datasets to show the suitability of the method to improve and simplify shape analysis and classification issues.
[Display omitted]
•Grouping sets of scalar functions according to their behaviour over a shape, or a shape class.•Selection of functions that are as much as possible selection independent.•Use of a clustering technique to learn representative functions.</description><identifier>ISSN: 0097-8493</identifier><identifier>EISSN: 1873-7684</identifier><identifier>DOI: 10.1016/j.cag.2013.05.007</identifier><identifier>CODEN: COGRD2</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Applied sciences ; Artificial intelligence ; Classification ; Clustering ; Computer graphics ; Computer science; control theory; systems ; Exact sciences and technology ; Mathematical analysis ; Mathematical models ; Pattern recognition. Digital image processing. Computational geometry ; Scalar functions ; Scalars ; Shape classification ; Shape description ; Three dimensional</subject><ispartof>Computers & graphics, 2013-10, Vol.37 (6), p.608-619</ispartof><rights>2013 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-8b9189dffc23a9d3d049b253246348bb835c1e490e9c0ee42726a72bda355b6d3</citedby><cites>FETCH-LOGICAL-c393t-8b9189dffc23a9d3d049b253246348bb835c1e490e9c0ee42726a72bda355b6d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0097849313000812$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,3537,23911,23912,25120,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27728331$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Biasotti, Silvia</creatorcontrib><creatorcontrib>Spagnuolo, Michela</creatorcontrib><creatorcontrib>Falcidieno, Bianca</creatorcontrib><title>Grouping real functions defined on 3D surfaces</title><title>Computers & graphics</title><description>Scalar functions are widely used to support shape analysis and description. Their role is to sift the most significant shape information and to discard the irrelevant one, acting as a filter for the characteristics that will contribute to the description. Unfortunately, a single property, or function, is not sufficient to characterize a shape and there is not a method to automatically select the functions that better describe a 3D object. Given a set of scalar functions defined on the same object, in this paper we propose a practical approach to automatically group these functions and select a subset of functions that are as much as possible independent of each other. Experiments are exhibited for several datasets to show the suitability of the method to improve and simplify shape analysis and classification issues.
[Display omitted]
•Grouping sets of scalar functions according to their behaviour over a shape, or a shape class.•Selection of functions that are as much as possible selection independent.•Use of a clustering technique to learn representative functions.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Classification</subject><subject>Clustering</subject><subject>Computer graphics</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Scalar functions</subject><subject>Scalars</subject><subject>Shape classification</subject><subject>Shape description</subject><subject>Three dimensional</subject><issn>0097-8493</issn><issn>1873-7684</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkE9LwzAchoMoOKcfwFsvgpfW_G0SPMnUKQy86DmkyS8jo2tn0gp-ezs2POrpvTzv-8KD0DXBFcGkvttUzq4rigmrsKgwlidoRpRkpawVP0UzjLUsFdfsHF3kvMEYU1rzGaqWqR93sVsXCWxbhLFzQ-y7XHgIsQNf9F3BHos8pmAd5Et0Fmyb4eqYc_Tx_PS-eClXb8vXxcOqdEyzoVSNJkr7EBxlVnvmMdcNFYzymnHVNIoJR4BrDNphAE4lra2kjbdMiKb2bI5uD7u71H-OkAezjdlB29oO-jEbIgjjtaCi_h_lfPJAtFITSg6oS33OCYLZpbi16dsQbPYazcZMGs1eo8HCTBqnzs1x3mZn25Bs52L-LVIpqWKMTNz9gYNJy1eEZLKL0DnwMYEbjO_jHy8_V5GE8Q</recordid><startdate>20131001</startdate><enddate>20131001</enddate><creator>Biasotti, Silvia</creator><creator>Spagnuolo, Michela</creator><creator>Falcidieno, Bianca</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20131001</creationdate><title>Grouping real functions defined on 3D surfaces</title><author>Biasotti, Silvia ; Spagnuolo, Michela ; Falcidieno, Bianca</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-8b9189dffc23a9d3d049b253246348bb835c1e490e9c0ee42726a72bda355b6d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Classification</topic><topic>Clustering</topic><topic>Computer graphics</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Scalar functions</topic><topic>Scalars</topic><topic>Shape classification</topic><topic>Shape description</topic><topic>Three dimensional</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Biasotti, Silvia</creatorcontrib><creatorcontrib>Spagnuolo, Michela</creatorcontrib><creatorcontrib>Falcidieno, Bianca</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & graphics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Biasotti, Silvia</au><au>Spagnuolo, Michela</au><au>Falcidieno, Bianca</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Grouping real functions defined on 3D surfaces</atitle><jtitle>Computers & graphics</jtitle><date>2013-10-01</date><risdate>2013</risdate><volume>37</volume><issue>6</issue><spage>608</spage><epage>619</epage><pages>608-619</pages><issn>0097-8493</issn><eissn>1873-7684</eissn><coden>COGRD2</coden><abstract>Scalar functions are widely used to support shape analysis and description. Their role is to sift the most significant shape information and to discard the irrelevant one, acting as a filter for the characteristics that will contribute to the description. Unfortunately, a single property, or function, is not sufficient to characterize a shape and there is not a method to automatically select the functions that better describe a 3D object. Given a set of scalar functions defined on the same object, in this paper we propose a practical approach to automatically group these functions and select a subset of functions that are as much as possible independent of each other. Experiments are exhibited for several datasets to show the suitability of the method to improve and simplify shape analysis and classification issues.
[Display omitted]
•Grouping sets of scalar functions according to their behaviour over a shape, or a shape class.•Selection of functions that are as much as possible selection independent.•Use of a clustering technique to learn representative functions.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cag.2013.05.007</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0097-8493 |
| ispartof | Computers & graphics, 2013-10, Vol.37 (6), p.608-619 |
| issn | 0097-8493 1873-7684 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1513465256 |
| source | Elsevier ScienceDirect Journals |
| subjects | Applied sciences Artificial intelligence Classification Clustering Computer graphics Computer science control theory systems Exact sciences and technology Mathematical analysis Mathematical models Pattern recognition. Digital image processing. Computational geometry Scalar functions Scalars Shape classification Shape description Three dimensional |
| title | Grouping real functions defined on 3D surfaces |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-27T11%3A08%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Grouping%20real%20functions%20defined%20on%203D%20surfaces&rft.jtitle=Computers%20&%20graphics&rft.au=Biasotti,%20Silvia&rft.date=2013-10-01&rft.volume=37&rft.issue=6&rft.spage=608&rft.epage=619&rft.pages=608-619&rft.issn=0097-8493&rft.eissn=1873-7684&rft.coden=COGRD2&rft_id=info:doi/10.1016/j.cag.2013.05.007&rft_dat=%3Cproquest_cross%3E1513465256%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1448731988&rft_id=info:pmid/&rft_els_id=S0097849313000812&rfr_iscdi=true |