Generic polar harmonic transforms for invariant image representation
Identifieur interne : 002600 ( Hal/Corpus ); précédent : 002599; suivant : 002601Generic polar harmonic transforms for invariant image representation
Auteurs : Thai V. Hoang ; Salvatore TabboneSource :
- Image and Vision Computing [ 0262-8856 ] ; 2014.
English descriptors
Abstract
This paper introduces four classes of rotation-invariant orthogonal moments by generalizing four existing moments that use harmonic functions in their radial kernels. Members of these classes share beneficial properties for image representation and pattern recognition like orthogonality and rotation-invariance. The kernel sets of these generic harmonic function-based moments are complete in the Hilbert space of square-integrable continuous complex-valued functions. Due to their resemble definition, the computation of these kernels maintains the simplicity and numerical stability of existing harmonic function-based moments. In addition, each member of one of these classes has distinctive properties that depend on the value of a parameter, making it more suitable for some particular applications. Comparison with existing orthogonal moments defined based on Jacobi polynomials and eigenfunctions has been carried out and experimental results show the effectiveness of these classes of moments in terms of representation capability and discrimination power.
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DOI: 10.1016/j.imavis.2014.04.016
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Members of these classes share beneficial properties for image representation and pattern recognition like orthogonality and rotation-invariance. The kernel sets of these generic harmonic function-based moments are complete in the Hilbert space of square-integrable continuous complex-valued functions. Due to their resemble definition, the computation of these kernels maintains the simplicity and numerical stability of existing harmonic function-based moments. In addition, each member of one of these classes has distinctive properties that depend on the value of a parameter, making it more suitable for some particular applications. Comparison with existing orthogonal moments defined based on Jacobi polynomials and eigenfunctions has been carried out and experimental results show the effectiveness of these classes of moments in terms of representation capability and discrimination power.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Generic polar harmonic transforms for invariant image representation</title><author role="aut"><persName><forename type="first">Thai V.</forename><surname>Hoang</surname></persName><email>vanthai.hoang@loria.fr</email><ptr type="url" target="http://www.loria.fr/~hoangvva/"></ptr><idno type="idhal">hoang</idno><idno type="halauthor">741559</idno><affiliation ref="#struct-93800"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>tabbone@loria.fr</email><idno type="halauthor">59043</idno><affiliation ref="#struct-206044"></affiliation></author><editor role="depositor"><persName><forename>Thai V.</forename><surname>Hoang</surname></persName><email>hoang@igbmc.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2014-11-17 17:50:22</date><date type="whenModified">2015-09-22 01:11:04</date><date type="whenReleased">2014-11-18 16:27:26</date><date type="whenProduced">2014</date><date type="whenEndEmbargoed">2014-11-17</date><ref type="file" target="https://hal.inria.fr/hal-01083722/document"><date notBefore="2014-11-17"></date></ref><ref type="file" subtype="author" n="1" target="https://hal.inria.fr/hal-01083722/file/Moment_har_def_final.pdf"><date notBefore="2014-11-17"></date></ref></edition><respStmt><resp>contributor</resp><name key="145187"><persName><forename>Thai V.</forename><surname>Hoang</surname></persName><email>hoang@igbmc.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">hal-01083722</idno><idno type="halUri">https://hal.inria.fr/hal-01083722</idno><idno type="halBibtex">hoang:hal-01083722</idno><idno type="halRefHtml">Image and Vision Computing, Elsevier, 2014, 32 (8), pp.497 - 509. <10.1016/j.imavis.2014.04.016></idno><idno type="halRef">Image and Vision Computing, Elsevier, 2014, 32 (8), pp.497 - 509. <10.1016/j.imavis.2014.04.016></idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="IGBMC" p="INSERM">Institut Génétique de Biologie Moléculaire et Cellulaire</idno><idno type="stamp" n="UNIV-STRASBG">Université de Strasbourg</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Generic polar harmonic transforms for invariant image representation</title><author role="aut"><persName><forename type="first">Thai V.</forename><surname>Hoang</surname></persName><email>vanthai.hoang@loria.fr</email><ptr type="url" target="http://www.loria.fr/~hoangvva/"></ptr><idno type="idHal">hoang</idno><idno type="halAuthorId">741559</idno><affiliation ref="#struct-93800"></affiliation></author><author role="aut"><persName><forename type="first">Salvatore</forename><surname>Tabbone</surname></persName><email>tabbone@loria.fr</email><idno type="halAuthorId">59043</idno><affiliation ref="#struct-206044"></affiliation></author></analytic><monogr><idno type="halJournalId" status="VALID">14103</idno><idno type="issn">0262-8856</idno><title level="j">Image and Vision Computing</title><imprint><publisher>Elsevier</publisher><biblScope unit="volume">32</biblScope><biblScope unit="issue">8</biblScope><biblScope unit="pp">497 - 509</biblScope><date type="datePub">2014</date><date type="dateEpub">2014</date></imprint></monogr><idno type="doi">10.1016/j.imavis.2014.04.016</idno></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Orthogonal moments</term><term xml:lang="en">Polar harmonic transforms</term><term xml:lang="en">Harmonic kernels</term><term xml:lang="en">Rotation invariance</term></keywords><classCode scheme="halDomain" n="info.info-ti">Computer Science [cs]/Image Processing</classCode><classCode scheme="halTypology" n="ART">Journal articles</classCode></textClass><abstract xml:lang="en">This paper introduces four classes of rotation-invariant orthogonal moments by generalizing four existing moments that use harmonic functions in their radial kernels. Members of these classes share beneficial properties for image representation and pattern recognition like orthogonality and rotation-invariance. The kernel sets of these generic harmonic function-based moments are complete in the Hilbert space of square-integrable continuous complex-valued functions. Due to their resemble definition, the computation of these kernels maintains the simplicity and numerical stability of existing harmonic function-based moments. In addition, each member of one of these classes has distinctive properties that depend on the value of a parameter, making it more suitable for some particular applications. Comparison with existing orthogonal moments defined based on Jacobi polynomials and eigenfunctions has been carried out and experimental results show the effectiveness of these classes of moments in terms of representation capability and discrimination power.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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