Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network
Identifieur interne : 000434 ( Istex/Corpus ); précédent : 000433; suivant : 000435Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network
Auteurs : Congyan Lang ; De Xu ; Xu Yang ; Yiwei Jiang ; Wengang ChengSource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: In this paper, we propose a new approach for the prominent region detection from the viewpoint of the human perception intending to construct a good pattern for content representation of the video sequences. Firstly, we partition each frame into homogeneous regions using a technique based on a non-parameter clustering algorithm. Then, in order to automatically determine the prominent importance of the different homogenous regions in a frame, we extract a number of different mise-en-scene-based perceptual features, which influence human visual attention. Finally, a modified Fuzzy Inference Neural Network is used to detect prominent regions in video sequences, due to its simple structure and superior performance for automatic fuzzy rules extraction. The extracted prominent regions could be used as a good pattern to bridge semantic gap between low-level features and semantic understanding. Experimental results show the excellent performance of the approach.
Url:
DOI: 10.1007/11427445_132
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network</title><author><name sortKey="Lang, Congyan" sort="Lang, Congyan" uniqKey="Lang C" first="Congyan" last="Lang">Congyan Lang</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: gltree@263.net</mods:affiliation></affiliation></author><author><name sortKey="Xu, De" sort="Xu, De" uniqKey="Xu D" first="De" last="Xu">De Xu</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author><author><name sortKey="Yang, Xu" sort="Yang, Xu" uniqKey="Yang X" first="Xu" last="Yang">Xu Yang</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author><author><name sortKey="Jiang, Yiwei" sort="Jiang, Yiwei" uniqKey="Jiang Y" first="Yiwei" last="Jiang">Yiwei Jiang</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author><author><name sortKey="Cheng, Wengang" sort="Cheng, Wengang" uniqKey="Cheng W" first="Wengang" last="Cheng">Wengang Cheng</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:F4608B5C99D69B9A50664168C40E5A4520A9424F</idno><date when="2005" year="2005">2005</date><idno type="doi">10.1007/11427445_132</idno><idno type="url">https://api.istex.fr/document/F4608B5C99D69B9A50664168C40E5A4520A9424F/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">000434</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network</title><author><name sortKey="Lang, Congyan" sort="Lang, Congyan" uniqKey="Lang C" first="Congyan" last="Lang">Congyan Lang</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: gltree@263.net</mods:affiliation></affiliation></author><author><name sortKey="Xu, De" sort="Xu, De" uniqKey="Xu D" first="De" last="Xu">De Xu</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author><author><name sortKey="Yang, Xu" sort="Yang, Xu" uniqKey="Yang X" first="Xu" last="Yang">Xu Yang</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author><author><name sortKey="Jiang, Yiwei" sort="Jiang, Yiwei" uniqKey="Jiang Y" first="Yiwei" last="Jiang">Yiwei Jiang</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author><author><name sortKey="Cheng, Wengang" sort="Cheng, Wengang" uniqKey="Cheng W" first="Wengang" last="Cheng">Wengang Cheng</name><affiliation><mods:affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s">Lecture Notes in Computer Science</title><imprint><date>2005</date></imprint><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series><idno type="istex">F4608B5C99D69B9A50664168C40E5A4520A9424F</idno><idno type="DOI">10.1007/11427445_132</idno><idno type="ChapterID">132</idno><idno type="ChapterID">Chap132</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: In this paper, we propose a new approach for the prominent region detection from the viewpoint of the human perception intending to construct a good pattern for content representation of the video sequences. Firstly, we partition each frame into homogeneous regions using a technique based on a non-parameter clustering algorithm. Then, in order to automatically determine the prominent importance of the different homogenous regions in a frame, we extract a number of different mise-en-scene-based perceptual features, which influence human visual attention. Finally, a modified Fuzzy Inference Neural Network is used to detect prominent regions in video sequences, due to its simple structure and superior performance for automatic fuzzy rules extraction. The extracted prominent regions could be used as a good pattern to bridge semantic gap between low-level features and semantic understanding. Experimental results show the excellent performance of the approach.</div></front></TEI><istex><corpusName>springer</corpusName><author><json:item><name>Congyan Lang</name><affiliations><json:string>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</json:string><json:string>E-mail: gltree@263.net</json:string></affiliations></json:item><json:item><name>De Xu</name><affiliations><json:string>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</json:string></affiliations></json:item><json:item><name>Xu Yang</name><affiliations><json:string>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</json:string></affiliations></json:item><json:item><name>Yiwei Jiang</name><affiliations><json:string>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</json:string></affiliations></json:item><json:item><name>Wengang Cheng</name><affiliations><json:string>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</json:string></affiliations></json:item></author><language><json:string>eng</json:string></language><abstract>Abstract: In this paper, we propose a new approach for the prominent region detection from the viewpoint of the human perception intending to construct a good pattern for content representation of the video sequences. 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Firstly, we partition each frame into homogeneous regions using a technique based on a non-parameter clustering algorithm. Then, in order to automatically determine the prominent importance of the different homogenous regions in a frame, we extract a number of different mise-en-scene-based perceptual features, which influence human visual attention. Finally, a modified Fuzzy Inference Neural Network is used to detect prominent regions in video sequences, due to its simple structure and superior performance for automatic fuzzy rules extraction. The extracted prominent regions could be used as a good pattern to bridge semantic gap between low-level features and semantic understanding. Experimental results show the excellent performance of the approach.</p></abstract><textClass><keywords scheme="Book Subject Collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass><keywords scheme="Book Subject Group"><list><label>I</label><label>I16013</label><label>I13022</label><label>I16021</label><label>I17028</label><label>I21017</label><label>I2203X</label><item><term>Computer Science</term></item><item><term>Computation by Abstract Devices</term></item><item><term>Computer Communication Networks</term></item><item><term>Algorithm Analysis and Problem Complexity</term></item><item><term>Discrete Mathematics in Computer Science</term></item><item><term>Artificial Intelligence (incl. 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ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><City>MA</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>New York University</OrgName><OrgAddress><City>NY</City><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbruecken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingDepth="2" NumberingStyle="ContentOnly" OutputMedium="All" TocLevels="0"><BookID>978-3-540-32067-8</BookID><BookTitle>Advances in Neural Networks – ISNN 2005</BookTitle><BookSubTitle>Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II</BookSubTitle><BookVolumeNumber>3497</BookVolumeNumber><BookSequenceNumber>3497</BookSequenceNumber><BookDOI>10.1007/b136478</BookDOI><BookTitleID>118235</BookTitleID><BookPrintISBN>978-3-540-25913-8</BookPrintISBN><BookElectronicISBN>978-3-540-32067-8</BookElectronicISBN><BookChapterCount>150</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I16013" Priority="1" Type="Secondary">Computation by Abstract Devices</BookSubject><BookSubject Code="I13022" Priority="2" Type="Secondary">Computer Communication Networks</BookSubject><BookSubject Code="I16021" Priority="3" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><BookSubject Code="I17028" Priority="4" Type="Secondary">Discrete Mathematics in Computer Science</BookSubject><BookSubject Code="I21017" Priority="5" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I2203X" Priority="6" Type="Secondary">Pattern Recognition</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Jun</GivenName><FamilyName>Wang</FamilyName></EditorName><Contact><Email>jwang@mae.cuhk.edu.hk</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Xiao-Feng</GivenName><FamilyName>Liao</FamilyName></EditorName><Contact><Email>xfliao@cqu.edu.cn</Email></Contact></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Zhang</GivenName><FamilyName>Yi</FamilyName></EditorName><Contact><Email>zhangyi@uestc.edu.cn</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Mechanical and Automation Engineering</OrgDivision><OrgName>The Chinese University of Hong Kong, Shatin, New Territories</OrgName><OrgAddress><City>Hong Kong</City><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education</OrgName><OrgAddress><Country>China</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgDivision>Computational Intelligence Laboratory, School of Computer Science and Engineering</OrgDivision><OrgName>University of Electronic Science and Technology of China</OrgName><OrgAddress><Postcode>610054</Postcode><City>Chengdu</City><Country>P.R. China</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part4"><PartInfo TocLevels="0"><PartID>4</PartID><PartSequenceNumber>4</PartSequenceNumber><PartTitle>Image Processing</PartTitle><PartChapterCount>34</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Advances in Neural Networks – ISNN 2005</BookTitle></PartContext></PartInfo><Chapter ID="Chap132" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>132</ChapterID><ChapterDOI>10.1007/11427445_132</ChapterDOI><ChapterSequenceNumber>132</ChapterSequenceNumber><ChapterTitle Language="En">Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network</ChapterTitle><ChapterFirstPage>819</ChapterFirstPage><ChapterLastPage>827</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>4</PartID><BookID>978-3-540-32067-8</BookID><BookTitle>Advances in Neural Networks – ISNN 2005</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Congyan</GivenName><FamilyName>Lang</FamilyName></AuthorName><Contact><Email>gltree@263.net</Email></Contact></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>De</GivenName><FamilyName>Xu</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Xu</GivenName><FamilyName>Yang</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Yiwei</GivenName><FamilyName>Jiang</FamilyName></AuthorName></Author><Author AffiliationIDS="Aff19"><AuthorName DisplayOrder="Western"><GivenName>Wengang</GivenName><FamilyName>Cheng</FamilyName></AuthorName></Author><Affiliation ID="Aff19"><OrgDivision>Dept. of Computer Science</OrgDivision><OrgName>Beijing Jiaotong University</OrgName><OrgAddress><City>Beijing</City><Postcode>100044</Postcode><Country>China</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>In this paper, we propose a new approach for the prominent region detection from the viewpoint of the human perception intending to construct a good pattern for content representation of the video sequences. Firstly, we partition each frame into homogeneous regions using a technique based on a non-parameter clustering algorithm. Then, in order to automatically determine the prominent importance of the different homogenous regions in a frame, we extract a number of different mise-en-scene-based perceptual features, which influence human visual attention. Finally, a modified Fuzzy Inference Neural Network is used to detect prominent regions in video sequences, due to its simple structure and superior performance for automatic fuzzy rules extraction. The extracted prominent regions could be used as a good pattern to bridge semantic gap between low-level features and semantic understanding. Experimental results show the excellent performance of the approach.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network</title></titleInfo><name type="personal"><namePart type="given">Congyan</namePart><namePart type="family">Lang</namePart><affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</affiliation><affiliation>E-mail: gltree@263.net</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">De</namePart><namePart type="family">Xu</namePart><affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Xu</namePart><namePart type="family">Yang</namePart><affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Yiwei</namePart><namePart type="family">Jiang</namePart><affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Wengang</namePart><namePart type="family">Cheng</namePart><affiliation>Dept. of Computer Science, Beijing Jiaotong University, 100044, Beijing, China</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="conference [eBooks]" displayLabel="OriginalPaper"></genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2005</dateIssued><copyrightDate encoding="w3cdtf">2005</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Abstract: In this paper, we propose a new approach for the prominent region detection from the viewpoint of the human perception intending to construct a good pattern for content representation of the video sequences. Firstly, we partition each frame into homogeneous regions using a technique based on a non-parameter clustering algorithm. Then, in order to automatically determine the prominent importance of the different homogenous regions in a frame, we extract a number of different mise-en-scene-based perceptual features, which influence human visual attention. Finally, a modified Fuzzy Inference Neural Network is used to detect prominent regions in video sequences, due to its simple structure and superior performance for automatic fuzzy rules extraction. The extracted prominent regions could be used as a good pattern to bridge semantic gap between low-level features and semantic understanding. Experimental results show the excellent performance of the approach.</abstract><relatedItem type="host"><titleInfo><title>Advances in Neural Networks – ISNN 2005</title><subTitle>Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II</subTitle></titleInfo><name type="personal"><namePart type="given">Jun</namePart><namePart type="family">Wang</namePart><affiliation>Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China</affiliation><affiliation>E-mail: jwang@mae.cuhk.edu.hk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Xiao-Feng</namePart><namePart type="family">Liao</namePart><affiliation>The Key Laboratory of Optoelectric Technology & Systems, Ministry of Education, China</affiliation><affiliation>E-mail: xfliao@cqu.edu.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Zhang</namePart><namePart type="family">Yi</namePart><affiliation>Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China</affiliation><affiliation>E-mail: zhangyi@uestc.edu.cn</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="Book Series" displayLabel="Proceedings"></genre><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book Subject Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book Subject Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I16013">Computation by Abstract Devices</topic><topic authority="SpringerSubjectCodes" authorityURI="I13022">Computer Communication Networks</topic><topic authority="SpringerSubjectCodes" authorityURI="I16021">Algorithm Analysis and Problem Complexity</topic><topic authority="SpringerSubjectCodes" authorityURI="I17028">Discrete Mathematics in Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic></subject><identifier type="DOI">10.1007/b136478</identifier><identifier type="ISBN">978-3-540-25913-8</identifier><identifier type="eISBN">978-3-540-32067-8</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">118235</identifier><identifier type="BookID">978-3-540-32067-8</identifier><identifier type="BookChapterCount">150</identifier><identifier type="BookVolumeNumber">3497</identifier><identifier type="BookSequenceNumber">3497</identifier><identifier type="PartChapterCount">34</identifier><part><date>2005</date><detail type="part"><title>Image Processing</title></detail><detail type="volume"><number>3497</number><caption>vol.</caption></detail><extent unit="pages"><start>819</start><end>827</end></extent></part><recordInfo><recordOrigin>Springer-Verlag Berlin Heidelberg, 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NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moni</namePart><namePart type="family">Naor</namePart><affiliation>Weizmann Institute of Science, Rehovot, Israel</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Oscar</namePart><namePart type="family">Nierstrasz</namePart><affiliation>University of Bern, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">C.</namePart><namePart type="family">Pandu Rangan</namePart><affiliation>Indian Institute of Technology, Madras, India</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Bernhard</namePart><namePart type="family">Steffen</namePart><affiliation>University of Dortmund, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Madhu</namePart><namePart type="family">Sudan</namePart><affiliation>Massachusetts Institute of Technology, MA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Demetri</namePart><namePart type="family">Terzopoulos</namePart><affiliation>New York University, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dough</namePart><namePart type="family">Tygar</namePart><affiliation>University of California, Berkeley, CA, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Moshe</namePart><namePart type="given">Y.</namePart><namePart type="family">Vardi</namePart><affiliation>Rice University, Houston, TX, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Gerhard</namePart><namePart type="family">Weikum</namePart><affiliation>Max-Planck Institute of Computer Science, Saarbruecken, Germany</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><originInfo><copyrightDate encoding="w3cdtf">2005</copyrightDate><issuance>serial</issuance></originInfo><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer-Verlag Berlin 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|type= RBID
|clé= ISTEX:F4608B5C99D69B9A50664168C40E5A4520A9424F
|texte= Perception-Oriented Prominent Region Detection in Video Sequences Using Fuzzy Inference Neural Network
}}
| This area was generated with Dilib version V0.6.32. |  |