untitled
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<identifier>oai:HAL:hal-00634750v1</identifier>
<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Estimation of the Large Multifractal Deviation Spectrum</title>
<creator>Grandchamp, Enguerran</creator>
<creator>Abadi, Mohamed</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>SIGNAL-IMAGE-COMMUNICATION (SIC) ; Université de Poitiers - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>CAS Proceedings</source>
<source>CAS</source>
<coverage>GOSIER, France</coverage>
<identifier>hal-00634750</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00634750</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00634750</source>
<source>CAS, Nov 2006, GOSIER, France. pp.00, 2006</source>
<language>en</language>
<subject>[INFO.INFO-TI] Computer Science [cs]/Image Processing</subject>
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<type>Conference papers</type>
<description lang=en>This paper deals with texture classification using a multifractal approach. More precisely we analyse the singularity/regularity exponent that compose the textures because they theoretically carry most of the information. The analysis is made using the Legendre spectrum. Then a parameter vector is computed to describe this spectrum in order to classify the textures with an unsupervised k-means classifier. The resulting algorithm is evaluated against a classification directly based on the textures.</description>
<date>2006-11-14</date>
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