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Ashish Doshi, Jonathan Starck, and Adrian Hilton, An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video. JVRB - Journal of Virtual Reality and Broadcasting, 7(2010), no. 3. (urn:nbn:de:0009-6-25740)
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%0 Journal Article %T An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video %A Doshi, Ashish %A Starck, Jonathan %A Hilton, Adrian %J JVRB - Journal of Virtual Reality and Broadcasting %D 2010 %V 7(2010) %N 3 %@ 1860-2037 %F doshi2010 %X This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing. %L 004 %K Feature Matching %K Qualitative Analysis %K Recall-Accuracy %K Sift %K Video Sequences %R 10.20385/1860-2037/7.2010.3 %U http://nbn-resolving.de/urn:nbn:de:0009-6-25740 %U http://dx.doi.org/10.20385/1860-2037/7.2010.3Download
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@Article{doshi2010, author = "Doshi, Ashish and Starck, Jonathan and Hilton, Adrian", title = "An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video", journal = "JVRB - Journal of Virtual Reality and Broadcasting", year = "2010", volume = "7(2010)", number = "3", keywords = "Feature Matching; Qualitative Analysis; Recall-Accuracy; Sift; Video Sequences", abstract = "This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.", issn = "1860-2037", doi = "10.20385/1860-2037/7.2010.3", url = "http://nbn-resolving.de/urn:nbn:de:0009-6-25740" }Download
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TY - JOUR AU - Doshi, Ashish AU - Starck, Jonathan AU - Hilton, Adrian PY - 2010 DA - 2010// TI - An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video JO - JVRB - Journal of Virtual Reality and Broadcasting VL - 7(2010) IS - 3 KW - Feature Matching KW - Qualitative Analysis KW - Recall-Accuracy KW - Sift KW - Video Sequences AB - This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing. SN - 1860-2037 UR - http://nbn-resolving.de/urn:nbn:de:0009-6-25740 DO - 10.20385/1860-2037/7.2010.3 ID - doshi2010 ER -Download
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<?xml version="1.0" encoding="UTF-8"?> <b:Sources SelectedStyle="" xmlns:b="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" xmlns="http://schemas.openxmlformats.org/officeDocument/2006/bibliography" > <b:Source> <b:Tag>doshi2010</b:Tag> <b:SourceType>ArticleInAPeriodical</b:SourceType> <b:Year>2010</b:Year> <b:PeriodicalTitle>JVRB - Journal of Virtual Reality and Broadcasting</b:PeriodicalTitle> <b:Volume>7(2010)</b:Volume> <b:Issue>3</b:Issue> <b:Url>http://nbn-resolving.de/urn:nbn:de:0009-6-25740</b:Url> <b:Url>http://dx.doi.org/10.20385/1860-2037/7.2010.3</b:Url> <b:Author> <b:Author><b:NameList> <b:Person><b:Last>Doshi</b:Last><b:First>Ashish</b:First></b:Person> <b:Person><b:Last>Starck</b:Last><b:First>Jonathan</b:First></b:Person> <b:Person><b:Last>Hilton</b:Last><b:First>Adrian</b:First></b:Person> </b:NameList></b:Author> </b:Author> <b:Title>An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video</b:Title> <b:Comments>This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.</b:Comments> </b:Source> </b:Sources>Download
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PT Journal AU Doshi, A Starck, J Hilton, A TI An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video SO JVRB - Journal of Virtual Reality and Broadcasting PY 2010 VL 7(2010) IS 3 DI 10.20385/1860-2037/7.2010.3 DE Feature Matching; Qualitative Analysis; Recall-Accuracy; Sift; Video Sequences AB This paper presents an empirical study of affineinvariant feature detectors to perform matching onvideo sequences of people with non-rigid surfacedeformation. Recent advances in feature detection andwide baseline matching have focused on static scenes.Video frames of human movement capture highlynon-rigid deformation such as loose hair, cloth creases,skin stretching and free flowing clothing. This studyevaluates the performance of six widely used featuredetectors for sparse temporal correspondence on singleview and multiple view video sequences. Quantitativeevaluation is performed of both the number of featuresdetected and their temporal matching against and withoutground truth correspondence. Recall-accuracy analysis offeature matching is reported for temporal correspondenceon single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing. ERDownload
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<mods> <titleInfo> <title>An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video</title> </titleInfo> <name type="personal"> <namePart type="family">Doshi</namePart> <namePart type="given">Ashish</namePart> </name> <name type="personal"> <namePart type="family">Starck</namePart> <namePart type="given">Jonathan</namePart> </name> <name type="personal"> <namePart type="family">Hilton</namePart> <namePart type="given">Adrian</namePart> </name> <abstract>This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing.</abstract> <subject> <topic>Feature Matching</topic> <topic>Qualitative Analysis</topic> <topic>Recall-Accuracy</topic> <topic>Sift</topic> <topic>Video Sequences</topic> </subject> <classification authority="ddc">004</classification> <relatedItem type="host"> <genre authority="marcgt">periodical</genre> <genre>academic journal</genre> <titleInfo> <title>JVRB - Journal of Virtual Reality and Broadcasting</title> </titleInfo> <part> <detail type="volume"> <number>7(2010)</number> </detail> <detail type="issue"> <number>3</number> </detail> <date>2010</date> </part> </relatedItem> <identifier type="issn">1860-2037</identifier> <identifier type="urn">urn:nbn:de:0009-6-25740</identifier> <identifier type="doi">10.20385/1860-2037/7.2010.3</identifier> <identifier type="uri">http://nbn-resolving.de/urn:nbn:de:0009-6-25740</identifier> <identifier type="citekey">doshi2010</identifier> </mods>Download
Full Metadata
Bibliographic Citation | JVRB, 7(2010), no. 3. |
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Title |
An Empirical Study of Non-Rigid Surface Feature Matching of Human from 3D Video (eng) |
Author | Ashish Doshi, Jonathan Starck, Adrian Hilton |
Language | eng |
Abstract | This paper presents an empirical study of affine invariant feature detectors to perform matching on video sequences of people with non-rigid surface deformation. Recent advances in feature detection and wide baseline matching have focused on static scenes. Video frames of human movement capture highly non-rigid deformation such as loose hair, cloth creases, skin stretching and free flowing clothing. This study evaluates the performance of six widely used feature detectors for sparse temporal correspondence on single view and multiple view video sequences. Quantitative evaluation is performed of both the number of features detected and their temporal matching against and without ground truth correspondence. Recall-accuracy analysis of feature matching is reported for temporal correspondence on single view and multiple view sequences of people with variation in clothing and movement. This analysis identifies that existing feature detection and matching algorithms are unreliable for fast movement with common clothing. |
Subject | Feature Matching, Qualitative Analysis, Recall-Accuracy, Sift, Video Sequences |
Classified Subjects |
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DDC | 004 |
Rights | DPPL |
URN: | urn:nbn:de:0009-6-25740 |
DOI | https://doi.org/10.20385/1860-2037/7.2010.3 |