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<bibliography>
<biblioentry xreflabel="brunet2008dgci" id="brunet2008dgci">
   <authorgroup>
       <author><firstname>R&#233;my</firstname><lastname>Malgouyres</lastname></author>
       <author><firstname>Florent</firstname><lastname>Brunet</lastname></author>
       <author><firstname>S&#233;bastien</firstname><lastname>Fourey</lastname></author> 
       <editor><firstname>David</firstname><lastname>Coeurjolly</lastname></editor>
       <editor><firstname>Isabelle</firstname><lastname>Sivignon</lastname></editor>
       <editor><firstname>Laure</firstname><lastname>Tougne</lastname></editor>
       <editor><firstname>Florent</firstname><lastname>Dupont</lastname></editor> 
   </authorgroup>
   <citetitle pubwork="article">Binomial Convolutions and Derivatives Estimation from Noisy Discretizations</citetitle>

   <publisher>
      <publishername>Springer Berlin / Heidelberg</publishername>
   </publisher>
   <volumenum>4992</volumenum> 

   <artpagenums>370-379</artpagenums> 
   <pubdate>2008</pubdate>  
   <abstract>
      <para>We present a new method to estimate derivatives of digitized functions. Even with noisy data&#44; this approach is convergent and can be computed by using only the arithmetic operations. Moreover&#44; higher order derivatives can also be estimated. To deal with parametrized curves&#44; we introduce a new notion which solves the problem of correspondence between the parametrization of a continuous curve and the pixels numbering of a discrete object.
      </para>
   </abstract>
</biblioentry>
</bibliography>
