A two-stage robust statistical method for temporal registration from features of various type
Identifieur interne : 000997 ( Hal/Corpus ); précédent : 000996; suivant : 000998A two-stage robust statistical method for temporal registration from features of various type
Auteurs : Gilles Simon ; Marie-Odile BergerSource :
English descriptors
Abstract
A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. The heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way. It consists in using robust estimators in a two-stage process: a robust residual is computed for each feature and a robust function of these residuals is minimized. Our method is therefore able to give a correct estimate of the pose even when tracking errors occur. This method is also used to update the set of model features along the sequence, by finding the 2D contours that are tracked when new 3D features become visible. The reliability of the system has been shown on a 300-image sequence of a bridge shot at dusk time, for an augmented reality project.
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<front><div type="abstract" xml:lang="en">A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. The heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way. It consists in using robust estimators in a two-stage process: a robust residual is computed for each feature and a robust function of these residuals is minimized. Our method is therefore able to give a correct estimate of the pose even when tracking errors occur. This method is also used to update the set of model features along the sequence, by finding the 2D contours that are tracked when new 3D features become visible. The reliability of the system has been shown on a 300-image sequence of a bridge shot at dusk time, for an augmented reality project.</div>
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<idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno>
<idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno>
</seriesStmt>
<notesStmt><note type="audience" n="0">Not set</note>
<note type="report" n="6">Research Report</note>
</notesStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">A two-stage robust statistical method for temporal registration from features of various type</title>
<author role="aut"><persName><forename type="first">Gilles</forename>
<surname>Simon</surname>
</persName>
<idno type="halAuthorId">100927</idno>
<affiliation ref="#struct-2350"></affiliation>
</author>
<author role="aut"><persName><forename type="first">Marie-Odile</forename>
<surname>Berger</surname>
</persName>
<email>berger@loria.fr</email>
<idno type="halAuthorId">61390</idno>
<affiliation ref="#struct-2350"></affiliation>
</author>
</analytic>
<monogr><idno type="reportNumber">RR-3235</idno>
<imprint><date type="datePub">1997</date>
</imprint>
<authority type="institution">INRIA</authority>
</monogr>
</biblStruct>
</sourceDesc>
<profileDesc><langUsage><language ident="en">English</language>
</langUsage>
<textClass><keywords scheme="author"><term xml:lang="en">AUGMENTED REALITY</term>
<term xml:lang="en">TRACKING</term>
<term xml:lang="en">TEMPORAL REGISTRATION</term>
<term xml:lang="en">POSE COMPUTATION</term>
<term xml:lang="en">ROBUST ESTIMATION</term>
</keywords>
<classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode>
<classCode scheme="halTypology" n="REPORT">Reports</classCode>
</textClass>
<abstract xml:lang="en">A model registration system capable of tracking an object, the model of which is known, in an image sequence is presented. It integrates tracking, pose determination and updating of the visible features. The heart of our system is the pose computation method, which handles various features (points, lines and free-form curves) in a very robust way. It consists in using robust estimators in a two-stage process: a robust residual is computed for each feature and a robust function of these residuals is minimized. Our method is therefore able to give a correct estimate of the pose even when tracking errors occur. This method is also used to update the set of model features along the sequence, by finding the 2D contours that are tracked when new 3D features become visible. The reliability of the system has been shown on a 300-image sequence of a bridge shot at dusk time, for an augmented reality project.</abstract>
</profileDesc>
</hal>
</record>
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