Multivariate statistical modeling of images with the curvelet transform
This paper proposes a theoretical and statistical study to characterize the dependencies of the curvelet coefficients of images across position, scale and orientation. Our study was based on estimated histograms of the marginal and joint distributions to study the statistical properties of curvelet...
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| creator | Boubchir, L. Fadili, J.M. |
| description | This paper proposes a theoretical and statistical study to characterize the dependencies of the curvelet coefficients of images across position, scale and orientation. Our study was based on estimated histograms of the marginal and joint distributions to study the statistical properties of curvelet coefficients, and on the mutual information to measure the level of dependence between these coefficients. Finally, a novel multivariate statistical model, namely the anisotropic multivariate generalized gaussian (AMGGD), was proposed to characterize these dependencies. |
| doi_str_mv | 10.1109/ISSPA.2005.1581046 |
| format | Conference Proceeding |
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Our study was based on estimated histograms of the marginal and joint distributions to study the statistical properties of curvelet coefficients, and on the mutual information to measure the level of dependence between these coefficients. Finally, a novel multivariate statistical model, namely the anisotropic multivariate generalized gaussian (AMGGD), was proposed to characterize these dependencies.</description><identifier>ISBN: 9780780392434</identifier><identifier>ISBN: 0780392434</identifier><identifier>DOI: 10.1109/ISSPA.2005.1581046</identifier><language>eng</language><publisher>IEEE</publisher><subject>Anisotropic magnetoresistance ; Computer Science ; Histograms ; Image coding ; Image Processing ; Image representation ; Image restoration ; Mutual information ; Object detection ; Statistics ; Wavelet transforms</subject><ispartof>Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005, 2005, Vol.2, p.747-750</ispartof><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1581046$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,309,310,776,780,785,786,881,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1581046$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://hal.science/hal-01088628$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Boubchir, L.</creatorcontrib><creatorcontrib>Fadili, J.M.</creatorcontrib><title>Multivariate statistical modeling of images with the curvelet transform</title><title>Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005</title><addtitle>ISSPA</addtitle><description>This paper proposes a theoretical and statistical study to characterize the dependencies of the curvelet coefficients of images across position, scale and orientation. Our study was based on estimated histograms of the marginal and joint distributions to study the statistical properties of curvelet coefficients, and on the mutual information to measure the level of dependence between these coefficients. Finally, a novel multivariate statistical model, namely the anisotropic multivariate generalized gaussian (AMGGD), was proposed to characterize these dependencies.</description><subject>Anisotropic magnetoresistance</subject><subject>Computer Science</subject><subject>Histograms</subject><subject>Image coding</subject><subject>Image Processing</subject><subject>Image representation</subject><subject>Image restoration</subject><subject>Mutual information</subject><subject>Object detection</subject><subject>Statistics</subject><subject>Wavelet transforms</subject><isbn>9780780392434</isbn><isbn>0780392434</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kEFLw0AQhRdEUGr_gF726qFxJ8kmu8dStC1UFKrnMLuZbVaSRrLbiv_elBYfDwYe3wzDY-weRAIg9NN6u32fJ6kQMgGpQOTFFZvqUonRmU7zLL9h0xC-xKhMFzIXt2z5emijP-LgMRIPEaMP0VtsedfX1Pr9jveO-w53FPiPjw2PDXF7GI7UUuRxwH1w_dDdsWuHbaDpZU7Y58vzx2I127wt14v5ZtakMoszU1MJJBVJyo1ETabQhTXknJIpCouFBifBGYe5caXFUplxJy0JAKytswl7PN9tsK2-h_Gx4bfq0Ver-aY6ZQKEUkWqjjCyD2fWE9E_fKkm-wMM61uv</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Boubchir, L.</creator><creator>Fadili, J.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><scope>1XC</scope><scope>VOOES</scope></search><sort><creationdate>2005</creationdate><title>Multivariate statistical modeling of images with the curvelet transform</title><author>Boubchir, L. ; Fadili, J.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h253t-bde71e58e5e4b5a9eb696cbeff852a0ca691f51fbfa4bf7ca78bbde27e111ccd3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Anisotropic magnetoresistance</topic><topic>Computer Science</topic><topic>Histograms</topic><topic>Image coding</topic><topic>Image Processing</topic><topic>Image representation</topic><topic>Image restoration</topic><topic>Mutual information</topic><topic>Object detection</topic><topic>Statistics</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Boubchir, L.</creatorcontrib><creatorcontrib>Fadili, J.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Boubchir, L.</au><au>Fadili, J.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Multivariate statistical modeling of images with the curvelet transform</atitle><btitle>Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005</btitle><stitle>ISSPA</stitle><date>2005</date><risdate>2005</risdate><volume>2</volume><spage>747</spage><epage>750</epage><pages>747-750</pages><isbn>9780780392434</isbn><isbn>0780392434</isbn><abstract>This paper proposes a theoretical and statistical study to characterize the dependencies of the curvelet coefficients of images across position, scale and orientation. Our study was based on estimated histograms of the marginal and joint distributions to study the statistical properties of curvelet coefficients, and on the mutual information to measure the level of dependence between these coefficients. Finally, a novel multivariate statistical model, namely the anisotropic multivariate generalized gaussian (AMGGD), was proposed to characterize these dependencies.</abstract><pub>IEEE</pub><doi>10.1109/ISSPA.2005.1581046</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| language | eng |
| recordid | cdi_ieee_primary_1581046 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Anisotropic magnetoresistance Computer Science Histograms Image coding Image Processing Image representation Image restoration Mutual information Object detection Statistics Wavelet transforms |
| title | Multivariate statistical modeling of images with the curvelet transform |
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