A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights
Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with...
Gespeichert in:
| Veröffentlicht in: | Neural processing letters 2019-10, Vol.50 (2), p.1485-1502 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1502 |
|---|---|
| container_issue | 2 |
| container_start_page | 1485 |
| container_title | Neural processing letters |
| container_volume | 50 |
| creator | Song, Tao Pang, Shanchen Hao, Shaohua Rodríguez-Patón, Alfonso Zheng, Pan |
| description | Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with SN P systems with weights. Specifically, an SN P system with weighs is constructed to achieve the Zhang–Suen image skeletonizing algorithm. Instead of serial calculation like Zhang–Suen image skeletonizing algorithm, the proposed method can parallel process a certain number of pixels of an image by spiking multiple neurons simultaneously at any computation step. Demonstrating via the experimental results, our method shows higher efficiency in data-reduction and simpler skeletons with less noise spurs than the method developed in Diazpernil (Neurocomputing 115:81–91,
2013
) in skeletonizing images like hand-written words. |
| doi_str_mv | 10.1007/s11063-018-9947-9 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2918346612</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918346612</sourcerecordid><originalsourceid>FETCH-LOGICAL-c316t-d0fe27a82ab39c6f10197ae175f4039188ddb983468c62d4f62376c92dd650633</originalsourceid><addsrcrecordid>eNp1kN1LwzAUxYMoOKd_gG8Bn6O5SZc0j2P4MZhfzKFvIWvTrlvXziRD5l9vSgWffDr3wvmdyz0IXQK9BkrljQegghMKKVEqkUQdoQGMJCdS8o_jOHNJSSIYnKIz79eURorRAXod4xfjTF3bGk-3prR4vrG1DW1TfVdNiR9tWLU5Xvhume-qTadPdh8R_ILnBx_s1uOvKqzwu63KVfDn6KQwtbcXvzpEi7vbt8kDmT3fTyfjGck4iEByWlgmTcrMkqtMFEBBSWNBjoqEcgVpmudLlfJEpJlgeVIIxqXIFMtzMYqv8iG66nN3rv3cWx_0ut27Jp7ULOIRFMCiC3pX5lrvnS30zlVb4w4aqO6a031zOjanu-a0igzrGR-9TWndX_L_0A9Mhm9_</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2918346612</pqid></control><display><type>article</type><title>A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights</title><source>ProQuest Central UK/Ireland</source><source>SpringerLink Journals - AutoHoldings</source><source>ProQuest Central</source><creator>Song, Tao ; Pang, Shanchen ; Hao, Shaohua ; Rodríguez-Patón, Alfonso ; Zheng, Pan</creator><creatorcontrib>Song, Tao ; Pang, Shanchen ; Hao, Shaohua ; Rodríguez-Patón, Alfonso ; Zheng, Pan</creatorcontrib><description>Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with SN P systems with weights. Specifically, an SN P system with weighs is constructed to achieve the Zhang–Suen image skeletonizing algorithm. Instead of serial calculation like Zhang–Suen image skeletonizing algorithm, the proposed method can parallel process a certain number of pixels of an image by spiking multiple neurons simultaneously at any computation step. Demonstrating via the experimental results, our method shows higher efficiency in data-reduction and simpler skeletons with less noise spurs than the method developed in Diazpernil (Neurocomputing 115:81–91,
2013
) in skeletonizing images like hand-written words.</description><identifier>ISSN: 1370-4621</identifier><identifier>EISSN: 1573-773X</identifier><identifier>DOI: 10.1007/s11063-018-9947-9</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Artificial Intelligence ; Biomimetics ; Complex Systems ; Computation ; Computational Intelligence ; Computer Science ; Deep learning ; Neural networks ; Neurons ; Spiking</subject><ispartof>Neural processing letters, 2019-10, Vol.50 (2), p.1485-1502</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-d0fe27a82ab39c6f10197ae175f4039188ddb983468c62d4f62376c92dd650633</citedby><cites>FETCH-LOGICAL-c316t-d0fe27a82ab39c6f10197ae175f4039188ddb983468c62d4f62376c92dd650633</cites><orcidid>0000-0002-0130-3340</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/s11063-018-9947-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918346612?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,777,781,21369,27905,27906,33725,41469,42538,43786,51300,64364,64368,72218</link.rule.ids></links><search><creatorcontrib>Song, Tao</creatorcontrib><creatorcontrib>Pang, Shanchen</creatorcontrib><creatorcontrib>Hao, Shaohua</creatorcontrib><creatorcontrib>Rodríguez-Patón, Alfonso</creatorcontrib><creatorcontrib>Zheng, Pan</creatorcontrib><title>A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights</title><title>Neural processing letters</title><addtitle>Neural Process Lett</addtitle><description>Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with SN P systems with weights. Specifically, an SN P system with weighs is constructed to achieve the Zhang–Suen image skeletonizing algorithm. Instead of serial calculation like Zhang–Suen image skeletonizing algorithm, the proposed method can parallel process a certain number of pixels of an image by spiking multiple neurons simultaneously at any computation step. Demonstrating via the experimental results, our method shows higher efficiency in data-reduction and simpler skeletons with less noise spurs than the method developed in Diazpernil (Neurocomputing 115:81–91,
2013
) in skeletonizing images like hand-written words.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Biomimetics</subject><subject>Complex Systems</subject><subject>Computation</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Deep learning</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Spiking</subject><issn>1370-4621</issn><issn>1573-773X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kN1LwzAUxYMoOKd_gG8Bn6O5SZc0j2P4MZhfzKFvIWvTrlvXziRD5l9vSgWffDr3wvmdyz0IXQK9BkrljQegghMKKVEqkUQdoQGMJCdS8o_jOHNJSSIYnKIz79eURorRAXod4xfjTF3bGk-3prR4vrG1DW1TfVdNiR9tWLU5Xvhume-qTadPdh8R_ILnBx_s1uOvKqzwu63KVfDn6KQwtbcXvzpEi7vbt8kDmT3fTyfjGck4iEByWlgmTcrMkqtMFEBBSWNBjoqEcgVpmudLlfJEpJlgeVIIxqXIFMtzMYqv8iG66nN3rv3cWx_0ut27Jp7ULOIRFMCiC3pX5lrvnS30zlVb4w4aqO6a031zOjanu-a0igzrGR-9TWndX_L_0A9Mhm9_</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Song, Tao</creator><creator>Pang, Shanchen</creator><creator>Hao, Shaohua</creator><creator>Rodríguez-Patón, Alfonso</creator><creator>Zheng, Pan</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><orcidid>https://orcid.org/0000-0002-0130-3340</orcidid></search><sort><creationdate>20191001</creationdate><title>A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights</title><author>Song, Tao ; Pang, Shanchen ; Hao, Shaohua ; Rodríguez-Patón, Alfonso ; Zheng, Pan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c316t-d0fe27a82ab39c6f10197ae175f4039188ddb983468c62d4f62376c92dd650633</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Biomimetics</topic><topic>Complex Systems</topic><topic>Computation</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Deep learning</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Spiking</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Tao</creatorcontrib><creatorcontrib>Pang, Shanchen</creatorcontrib><creatorcontrib>Hao, Shaohua</creatorcontrib><creatorcontrib>Rodríguez-Patón, Alfonso</creatorcontrib><creatorcontrib>Zheng, Pan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest One Psychology</collection><jtitle>Neural processing letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Tao</au><au>Pang, Shanchen</au><au>Hao, Shaohua</au><au>Rodríguez-Patón, Alfonso</au><au>Zheng, Pan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights</atitle><jtitle>Neural processing letters</jtitle><stitle>Neural Process Lett</stitle><date>2019-10-01</date><risdate>2019</risdate><volume>50</volume><issue>2</issue><spage>1485</spage><epage>1502</epage><pages>1485-1502</pages><issn>1370-4621</issn><eissn>1573-773X</eissn><abstract>Spiking neural P systems (namely SN P systems, for short) are bio-inspired neural-like computing models under the framework of membrane computing, which are also known as a new candidate of the third generation of neural networks. In this work, a parallel image skeletonizing method is proposed with SN P systems with weights. Specifically, an SN P system with weighs is constructed to achieve the Zhang–Suen image skeletonizing algorithm. Instead of serial calculation like Zhang–Suen image skeletonizing algorithm, the proposed method can parallel process a certain number of pixels of an image by spiking multiple neurons simultaneously at any computation step. Demonstrating via the experimental results, our method shows higher efficiency in data-reduction and simpler skeletons with less noise spurs than the method developed in Diazpernil (Neurocomputing 115:81–91,
2013
) in skeletonizing images like hand-written words.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11063-018-9947-9</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-0130-3340</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1370-4621 |
| ispartof | Neural processing letters, 2019-10, Vol.50 (2), p.1485-1502 |
| issn | 1370-4621 1573-773X |
| language | eng |
| recordid | cdi_proquest_journals_2918346612 |
| source | ProQuest Central UK/Ireland; SpringerLink Journals - AutoHoldings; ProQuest Central |
| subjects | Algorithms Artificial Intelligence Biomimetics Complex Systems Computation Computational Intelligence Computer Science Deep learning Neural networks Neurons Spiking |
| title | A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-19T14%3A26%3A42IST&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=A%20Parallel%20Image%20Skeletonizing%20Method%20Using%20Spiking%20Neural%20P%20Systems%20with%20Weights&rft.jtitle=Neural%20processing%20letters&rft.au=Song,%20Tao&rft.date=2019-10-01&rft.volume=50&rft.issue=2&rft.spage=1485&rft.epage=1502&rft.pages=1485-1502&rft.issn=1370-4621&rft.eissn=1573-773X&rft_id=info:doi/10.1007/s11063-018-9947-9&rft_dat=%3Cproquest_cross%3E2918346612%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=2918346612&rft_id=info:pmid/&rfr_iscdi=true |