Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions
In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use...
Gespeichert in:
| Veröffentlicht in: | Journal of Information Science and Engineering 2008-05, Vol.24 (3), p.785-800 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 800 |
|---|---|
| container_issue | 3 |
| container_start_page | 785 |
| container_title | Journal of Information Science and Engineering |
| container_volume | 24 |
| creator | 唐政元(Cheng-Yuan Tang) 吳怡樂(Yi-Leh Wu) 賴岳宏(Yueh-Hung Lai) |
| description | In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do. |
| doi_str_mv | 10.6688/JISE.2008.24.3.8 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pasca</sourceid><recordid>TN_cdi_proquest_miscellaneous_33555837</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><airiti_id>10162364_200805_24_3_785_800_a</airiti_id><sourcerecordid>33555837</sourcerecordid><originalsourceid>FETCH-LOGICAL-a262t-a1383b745d235e4fb004a62a9fcf6c43c6ebe1a077366ca0346e3a1a31c92dca3</originalsourceid><addsrcrecordid>eNotkD1PwzAURTOARCnsjF5gS3D8bMcdq6qFolYdoAuL9eI6xVU-SuwU-u9JVKarKx1d6dwoekhpIqVSz2_L93nCKFUJ4wkk6ioapTSVMQPJb6Jb7w-UMik4H0Wfi67eYWXrgCVZY2jdL5n74CoMrqnJ1rt6T-anpuyGju2ZTMt907rwVXny0wdZd2Vw8SY_WBPcyZJ-0Aysv4uuCyy9vf_PcbRdzD9mr_Fq87KcTVcxMslCjCkoyDMudgyE5UVOKUfJcFKYQhoORtrcpkizDKQ0SIFLC5gipGbCdgZhHD1ddo9t891ZH3TlvLFlibVtOq8BhBAKsh58_AfRGyyLFmvjvD62vW171oyCEjChPScuHLpe1OlD07V1b6CHE4cP9fAtFZpxDTpTQitKNcIfBpFyhw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>33555837</pqid></control><display><type>article</type><title>Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>唐政元(Cheng-Yuan Tang) ; 吳怡樂(Yi-Leh Wu) ; 賴岳宏(Yueh-Hung Lai)</creator><creatorcontrib>唐政元(Cheng-Yuan Tang) ; 吳怡樂(Yi-Leh Wu) ; 賴岳宏(Yueh-Hung Lai)</creatorcontrib><description>In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do.</description><identifier>ISSN: 1016-2364</identifier><identifier>DOI: 10.6688/JISE.2008.24.3.8</identifier><language>eng</language><publisher>Taipei: 社團法人中華民國計算語言學學會</publisher><subject>Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Exact sciences and technology ; Experimental design ; Mathematics ; Pattern recognition. Digital image processing. Computational geometry ; Probability and statistics ; Sciences and techniques of general use ; Statistics</subject><ispartof>Journal of Information Science and Engineering, 2008-05, Vol.24 (3), p.785-800</ispartof><rights>2008 INIST-CNRS</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20385390$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>唐政元(Cheng-Yuan Tang)</creatorcontrib><creatorcontrib>吳怡樂(Yi-Leh Wu)</creatorcontrib><creatorcontrib>賴岳宏(Yueh-Hung Lai)</creatorcontrib><title>Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions</title><title>Journal of Information Science and Engineering</title><description>In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Experimental design</subject><subject>Mathematics</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Probability and statistics</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><issn>1016-2364</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><recordid>eNotkD1PwzAURTOARCnsjF5gS3D8bMcdq6qFolYdoAuL9eI6xVU-SuwU-u9JVKarKx1d6dwoekhpIqVSz2_L93nCKFUJ4wkk6ioapTSVMQPJb6Jb7w-UMik4H0Wfi67eYWXrgCVZY2jdL5n74CoMrqnJ1rt6T-anpuyGju2ZTMt907rwVXny0wdZd2Vw8SY_WBPcyZJ-0Aysv4uuCyy9vf_PcbRdzD9mr_Fq87KcTVcxMslCjCkoyDMudgyE5UVOKUfJcFKYQhoORtrcpkizDKQ0SIFLC5gipGbCdgZhHD1ddo9t891ZH3TlvLFlibVtOq8BhBAKsh58_AfRGyyLFmvjvD62vW171oyCEjChPScuHLpe1OlD07V1b6CHE4cP9fAtFZpxDTpTQitKNcIfBpFyhw</recordid><startdate>20080501</startdate><enddate>20080501</enddate><creator>唐政元(Cheng-Yuan Tang)</creator><creator>吳怡樂(Yi-Leh Wu)</creator><creator>賴岳宏(Yueh-Hung Lai)</creator><general>社團法人中華民國計算語言學學會</general><general>Institute of Information Science, Academia sinica</general><scope>188</scope><scope>IQODW</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20080501</creationdate><title>Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions</title><author>唐政元(Cheng-Yuan Tang) ; 吳怡樂(Yi-Leh Wu) ; 賴岳宏(Yueh-Hung Lai)</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a262t-a1383b745d235e4fb004a62a9fcf6c43c6ebe1a077366ca0346e3a1a31c92dca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Experimental design</topic><topic>Mathematics</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Probability and statistics</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><toplevel>online_resources</toplevel><creatorcontrib>唐政元(Cheng-Yuan Tang)</creatorcontrib><creatorcontrib>吳怡樂(Yi-Leh Wu)</creatorcontrib><creatorcontrib>賴岳宏(Yueh-Hung Lai)</creatorcontrib><collection>Airiti Library</collection><collection>Pascal-Francis</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>Journal of Information Science and Engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>唐政元(Cheng-Yuan Tang)</au><au>吳怡樂(Yi-Leh Wu)</au><au>賴岳宏(Yueh-Hung Lai)</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions</atitle><jtitle>Journal of Information Science and Engineering</jtitle><date>2008-05-01</date><risdate>2008</risdate><volume>24</volume><issue>3</issue><spage>785</spage><epage>800</epage><pages>785-800</pages><issn>1016-2364</issn><abstract>In this paper, we present the use of two evolutionary algorithms to estimate fundamental matrices. We first propose a modification of the Hybrid Taguchi Genetic Algorithm (HTGA) that employs a single objective function, either geometric or algebraic distance, for optimization. We then propose to use a multi-objective optimization algorithm, Intelligent Multi-Objective Evolutionary Algorithm (IMOEA), to optimize both geometric and algebraic distances concurrently. Our experiments show that the proposed modified HTGA (MHTGA) and IMOEA produce more accurate estimation of fundamental matrices than the traditional Genetic Algorithm (GA) and the original HTGA do.</abstract><cop>Taipei</cop><pub>社團法人中華民國計算語言學學會</pub><doi>10.6688/JISE.2008.24.3.8</doi><tpages>16</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1016-2364 |
| ispartof | Journal of Information Science and Engineering, 2008-05, Vol.24 (3), p.785-800 |
| issn | 1016-2364 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_33555837 |
| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Experimental design Mathematics Pattern recognition. Digital image processing. Computational geometry Probability and statistics Sciences and techniques of general use Statistics |
| title | Fundamental Matrix Estimation Using Evolutionary Algorithms with Multi-Objective Functions |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T01%3A13%3A00IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pasca&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fundamental%20Matrix%20Estimation%20Using%20Evolutionary%20Algorithms%20with%20Multi-Objective%20Functions&rft.jtitle=Journal%20of%20Information%20Science%20and%20Engineering&rft.au=%E5%94%90%E6%94%BF%E5%85%83(Cheng-Yuan%20Tang)&rft.date=2008-05-01&rft.volume=24&rft.issue=3&rft.spage=785&rft.epage=800&rft.pages=785-800&rft.issn=1016-2364&rft_id=info:doi/10.6688/JISE.2008.24.3.8&rft_dat=%3Cproquest_pasca%3E33555837%3C/proquest_pasca%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=33555837&rft_id=info:pmid/&rft_airiti_id=10162364_200805_24_3_785_800_a&rfr_iscdi=true |