Interactive, Mobile, Distributed Pattern Recognition
Identifieur interne : 000135 ( Istex/Corpus ); précédent : 000134; suivant : 000136Interactive, Mobile, Distributed Pattern Recognition
Auteurs : George NagySource :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2005.
Abstract
Abstract: As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative approaches. Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. Recent technological advances like pocket computers, camera phones and wireless networks will have greater influence on mobile, distributed, interactive recognition of natural patterns than on conventional high-volume applications like mail sorting , check reading or forms processing.
Url:
DOI: 10.1007/11553595_5
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ISTEX:ED6C553D28C60615084923FCAB4F0A5FB8FED11ALe document en format XML
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Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. 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Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. 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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>978-3-540-31866-8</BookID><BookTitle>Image Analysis and Processing – ICIAP 2005</BookTitle><BookSubTitle>13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings</BookSubTitle><BookVolumeNumber>3617</BookVolumeNumber><BookSequenceNumber>3617</BookSequenceNumber><BookDOI>10.1007/11553595</BookDOI><BookTitleID>126473</BookTitleID><BookPrintISBN>978-3-540-28869-5</BookPrintISBN><BookElectronicISBN>978-3-540-31866-8</BookElectronicISBN><BookChapterCount>148</BookChapterCount><BookCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2005</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I2203X" Priority="1" Type="Secondary">Pattern Recognition</BookSubject><BookSubject Code="I22021" Priority="2" Type="Secondary">Image Processing and Computer Vision</BookSubject><BookSubject Code="I21017" Priority="3" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I22013" Priority="4" Type="Secondary">Computer Graphics</BookSubject><BookSubject Code="I16021" Priority="5" Type="Secondary">Algorithm Analysis and Problem Complexity</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Fabio</GivenName><FamilyName>Roli</FamilyName></EditorName><Contact><Email>roli@diee.unica.it</Email></Contact></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Sergio</GivenName><FamilyName>Vitulano</FamilyName></EditorName><Contact><Email>vitulano@pacs.unica.it</Email></Contact></Editor><Affiliation ID="Aff16"><OrgDivision>Department of Electrical and Electronic Engineering, Piazza d’Armi</OrgDivision><OrgName>University of Cagliari</OrgName><OrgAddress><Postcode>09123</Postcode><City>Cagliari</City><Country>Italy</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>Università di Cagliari</OrgName><OrgAddress><Country> </Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Invited Papers</PartTitle><PartChapterCount>5</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Image Analysis and Processing – ICIAP 2005</BookTitle></PartContext></PartInfo><Chapter ID="Chap5" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingDepth="2" NumberingStyle="ContentOnly" TocLevels="0"><ChapterID>5</ChapterID><ChapterDOI>10.1007/11553595_5</ChapterDOI><ChapterSequenceNumber>5</ChapterSequenceNumber><ChapterTitle Language="En">Interactive, Mobile, Distributed Pattern Recognition</ChapterTitle><ChapterFirstPage>37</ChapterFirstPage><ChapterLastPage>49</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>1</PartID><BookID>978-3-540-31866-8</BookID><BookTitle>Image Analysis and Processing – ICIAP 2005</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff18"><AuthorName DisplayOrder="Western"><GivenName>George</GivenName><FamilyName>Nagy</FamilyName></AuthorName><Contact><Email>nagy@ecse.rpi.edu</Email></Contact></Author><Affiliation ID="Aff18"><OrgDivision>DocLab</OrgDivision><OrgName>Rensselaer Polytechnic Institute</OrgName><OrgAddress><City>Troy</City><State>NY</State><Postcode>12180</Postcode><Country>USA</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative approaches. Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. Recent technological advances like pocket computers, camera phones and wireless networks will have greater influence on mobile, distributed, interactive recognition of natural patterns than on conventional high-volume applications like mail sorting , check reading or forms processing.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Interactive, Mobile, Distributed Pattern Recognition</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Interactive, Mobile, Distributed Pattern Recognition</title></titleInfo><name type="personal"><namePart type="given">George</namePart><namePart type="family">Nagy</namePart><affiliation>DocLab, Rensselaer Polytechnic Institute, 12180, Troy, NY, USA</affiliation><affiliation>E-mail: nagy@ecse.rpi.edu</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: As the accuracy of conventional classifiers, based only on a static partitioning of feature space, appears to be approaching a limit, it may be useful to consider alternative approaches. Interactive classification is often more accurate then algorithmic classification, and requires less time than the unaided human. It is more suitable for the recognition of natural patterns in a narrow domain like trees, weeds or faces than for symbolic patterns like letters and phonemes. On the other hand, symbolic patterns lend themselves better to using context and style to recognize entire fields instead of individual patterns. Algorithmic learning and adaptation is facilitated by accurate statistics gleaned from large samples in the case of symbolic patterns, and by skilled human judgment in the case of natural patterns. Recent technological advances like pocket computers, camera phones and wireless networks will have greater influence on mobile, distributed, interactive recognition of natural patterns than on conventional high-volume applications like mail sorting , check reading or forms processing.</abstract><relatedItem type="host"><titleInfo><title>Image Analysis and Processing – ICIAP 2005</title><subTitle>13th International Conference, Cagliari, Italy, September 6-8, 2005. Proceedings</subTitle></titleInfo><name type="personal"><namePart type="given">Fabio</namePart><namePart type="family">Roli</namePart><affiliation>Department of Electrical and Electronic Engineering, Piazza d’Armi, University of Cagliari, 09123, Cagliari, Italy</affiliation><affiliation>E-mail: roli@diee.unica.it</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Sergio</namePart><namePart type="family">Vitulano</namePart><affiliation>Università di Cagliari</affiliation><affiliation>E-mail: vitulano@pacs.unica.it</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="I2203X">Pattern Recognition</topic><topic authority="SpringerSubjectCodes" authorityURI="I22021">Image Processing and Computer Vision</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. 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type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, 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 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