Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields
In this paper we present a statistical model with a nonsymmetric half-plane (NSHP) region of support for two-dimensional continuous-valued vector fields. It has the simplicity, efficiency, and ease of use of the well-known hidden Markov model (HMM) and associated Baum-Welch algorithms for time-serie...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 2011-04, Vol.47 (2), p.1073-1080 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1080 |
|---|---|
| container_issue | 2 |
| container_start_page | 1073 |
| container_title | IEEE transactions on aerospace and electronic systems |
| container_volume | 47 |
| creator | Baggenstoss, Paul M |
| description | In this paper we present a statistical model with a nonsymmetric half-plane (NSHP) region of support for two-dimensional continuous-valued vector fields. It has the simplicity, efficiency, and ease of use of the well-known hidden Markov model (HMM) and associated Baum-Welch algorithms for time-series and other one-dimensional problems. At the same time it is able to learn textures on a two-dimensional field. We describe a fast approximate forward procedure for computation of the joint probability density function (pdf) of the vector field as well as an approximate Baum-Welch algorithm for parameter reestimation. Radar and sonar applications include classification of two-dimensional fields such as range versus azimuth or range versus aspect angle data wherein each data point in the field consists of a multi-dimensional feature vector. We test the method using synthetic textures. |
| doi_str_mv | 10.1109/TAES.2011.5751243 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_862806560</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5751243</ieee_id><sourcerecordid>2325407721</sourcerecordid><originalsourceid>FETCH-LOGICAL-c325t-a68099111857e8243024f4b3c751392c6eeba42dac9f295d136ad6808a3e40073</originalsourceid><addsrcrecordid>eNpdkMFOwzAMhiMEEmPwAIhLxIlLR5w0aXJEgwESgwNj1yhrXSnQNdC0oL09mQYcOFmWv9-yP0JOgU0AmLlcXN08TzgDmMhCAs_FHhmBlEVmFBP7ZMQY6MxwCYfkKMbX1OY6FyNiF18hu_ZrbKMPrWvona8qbOncdW_hk85DhQ2tQ0enjYvR1750fQJpqOk0tL1vhzDEbOmaASv6GHzc0CWWfQrMPDZVPCYHtWsinvzUMXmZ3Symd9nD0-399OohKwWXfeaUZsYAgJYF6nQ-43mdr0SZnhGGlwpx5XJeudLU3MgKhHJVymgnMGesEGNysdv73oWPAWNv1z6W2DSuxXShBVUAN1oYldDzf-hrGLr0e7Racc2UTMrGBHZQ2YUYO6zte-fXrttYYHZr3G6N261x-2M8Zc52GY-If_zv9Btke3tQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>862806560</pqid></control><display><type>article</type><title>Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields</title><source>IEEE Electronic Library (IEL)</source><creator>Baggenstoss, Paul M</creator><creatorcontrib>Baggenstoss, Paul M</creatorcontrib><description>In this paper we present a statistical model with a nonsymmetric half-plane (NSHP) region of support for two-dimensional continuous-valued vector fields. It has the simplicity, efficiency, and ease of use of the well-known hidden Markov model (HMM) and associated Baum-Welch algorithms for time-series and other one-dimensional problems. At the same time it is able to learn textures on a two-dimensional field. We describe a fast approximate forward procedure for computation of the joint probability density function (pdf) of the vector field as well as an approximate Baum-Welch algorithm for parameter reestimation. Radar and sonar applications include classification of two-dimensional fields such as range versus azimuth or range versus aspect angle data wherein each data point in the field consists of a multi-dimensional feature vector. We test the method using synthetic textures.</description><identifier>ISSN: 0018-9251</identifier><identifier>EISSN: 1557-9603</identifier><identifier>DOI: 10.1109/TAES.2011.5751243</identifier><identifier>CODEN: IEARAX</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Data models ; Hidden Markov models ; Joints ; Markov processes ; Mathematical analysis ; Mathematical models ; Noise measurement ; Pixel ; Probability density functions ; Studies ; Support vector machine classification ; Surface layer ; Texture ; Two dimensional ; Vectors (mathematics)</subject><ispartof>IEEE transactions on aerospace and electronic systems, 2011-04, Vol.47 (2), p.1073-1080</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Apr 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-a68099111857e8243024f4b3c751392c6eeba42dac9f295d136ad6808a3e40073</citedby><cites>FETCH-LOGICAL-c325t-a68099111857e8243024f4b3c751392c6eeba42dac9f295d136ad6808a3e40073</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5751243$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5751243$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Baggenstoss, Paul M</creatorcontrib><title>Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields</title><title>IEEE transactions on aerospace and electronic systems</title><addtitle>T-AES</addtitle><description>In this paper we present a statistical model with a nonsymmetric half-plane (NSHP) region of support for two-dimensional continuous-valued vector fields. It has the simplicity, efficiency, and ease of use of the well-known hidden Markov model (HMM) and associated Baum-Welch algorithms for time-series and other one-dimensional problems. At the same time it is able to learn textures on a two-dimensional field. We describe a fast approximate forward procedure for computation of the joint probability density function (pdf) of the vector field as well as an approximate Baum-Welch algorithm for parameter reestimation. Radar and sonar applications include classification of two-dimensional fields such as range versus azimuth or range versus aspect angle data wherein each data point in the field consists of a multi-dimensional feature vector. We test the method using synthetic textures.</description><subject>Algorithms</subject><subject>Data models</subject><subject>Hidden Markov models</subject><subject>Joints</subject><subject>Markov processes</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Noise measurement</subject><subject>Pixel</subject><subject>Probability density functions</subject><subject>Studies</subject><subject>Support vector machine classification</subject><subject>Surface layer</subject><subject>Texture</subject><subject>Two dimensional</subject><subject>Vectors (mathematics)</subject><issn>0018-9251</issn><issn>1557-9603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkMFOwzAMhiMEEmPwAIhLxIlLR5w0aXJEgwESgwNj1yhrXSnQNdC0oL09mQYcOFmWv9-yP0JOgU0AmLlcXN08TzgDmMhCAs_FHhmBlEVmFBP7ZMQY6MxwCYfkKMbX1OY6FyNiF18hu_ZrbKMPrWvona8qbOncdW_hk85DhQ2tQ0enjYvR1750fQJpqOk0tL1vhzDEbOmaASv6GHzc0CWWfQrMPDZVPCYHtWsinvzUMXmZ3Symd9nD0-399OohKwWXfeaUZsYAgJYF6nQ-43mdr0SZnhGGlwpx5XJeudLU3MgKhHJVymgnMGesEGNysdv73oWPAWNv1z6W2DSuxXShBVUAN1oYldDzf-hrGLr0e7Racc2UTMrGBHZQ2YUYO6zte-fXrttYYHZr3G6N261x-2M8Zc52GY-If_zv9Btke3tQ</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Baggenstoss, Paul M</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>L7M</scope><scope>F28</scope></search><sort><creationdate>201104</creationdate><title>Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields</title><author>Baggenstoss, Paul M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-a68099111857e8243024f4b3c751392c6eeba42dac9f295d136ad6808a3e40073</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Algorithms</topic><topic>Data models</topic><topic>Hidden Markov models</topic><topic>Joints</topic><topic>Markov processes</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Noise measurement</topic><topic>Pixel</topic><topic>Probability density functions</topic><topic>Studies</topic><topic>Support vector machine classification</topic><topic>Surface layer</topic><topic>Texture</topic><topic>Two dimensional</topic><topic>Vectors (mathematics)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Baggenstoss, Paul M</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on aerospace and electronic systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Baggenstoss, Paul M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields</atitle><jtitle>IEEE transactions on aerospace and electronic systems</jtitle><stitle>T-AES</stitle><date>2011-04</date><risdate>2011</risdate><volume>47</volume><issue>2</issue><spage>1073</spage><epage>1080</epage><pages>1073-1080</pages><issn>0018-9251</issn><eissn>1557-9603</eissn><coden>IEARAX</coden><abstract>In this paper we present a statistical model with a nonsymmetric half-plane (NSHP) region of support for two-dimensional continuous-valued vector fields. It has the simplicity, efficiency, and ease of use of the well-known hidden Markov model (HMM) and associated Baum-Welch algorithms for time-series and other one-dimensional problems. At the same time it is able to learn textures on a two-dimensional field. We describe a fast approximate forward procedure for computation of the joint probability density function (pdf) of the vector field as well as an approximate Baum-Welch algorithm for parameter reestimation. Radar and sonar applications include classification of two-dimensional fields such as range versus azimuth or range versus aspect angle data wherein each data point in the field consists of a multi-dimensional feature vector. We test the method using synthetic textures.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TAES.2011.5751243</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 0018-9251 |
| ispartof | IEEE transactions on aerospace and electronic systems, 2011-04, Vol.47 (2), p.1073-1080 |
| issn | 0018-9251 1557-9603 |
| language | eng |
| recordid | cdi_proquest_journals_862806560 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Data models Hidden Markov models Joints Markov processes Mathematical analysis Mathematical models Noise measurement Pixel Probability density functions Studies Support vector machine classification Surface layer Texture Two dimensional Vectors (mathematics) |
| title | Two-Dimensional Hidden Markov Model for Classification of Continuous-Valued Noisy Vector Fields |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T07%3A36%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Two-Dimensional%20Hidden%20Markov%20Model%20for%20Classification%20of%20Continuous-Valued%20Noisy%20Vector%20Fields&rft.jtitle=IEEE%20transactions%20on%20aerospace%20and%20electronic%20systems&rft.au=Baggenstoss,%20Paul%20M&rft.date=2011-04&rft.volume=47&rft.issue=2&rft.spage=1073&rft.epage=1080&rft.pages=1073-1080&rft.issn=0018-9251&rft.eissn=1557-9603&rft.coden=IEARAX&rft_id=info:doi/10.1109/TAES.2011.5751243&rft_dat=%3Cproquest_RIE%3E2325407721%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=862806560&rft_id=info:pmid/&rft_ieee_id=5751243&rfr_iscdi=true |