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<bibliography>
<biblioentry xreflabel="brunet2010vmvroi" id="brunet2010vmvroi">
   <authorgroup>
       <author><firstname>Florent</firstname><lastname>Brunet</lastname></author>
       <author><firstname>Adrien</firstname><lastname>Bartoli</lastname></author>
       <author><firstname>Nassir</firstname><lastname>Navab</lastname></author>
       <author><firstname>R&#38;&#35;x00E9;my</firstname><lastname>Malgouyres</lastname></author> 
       <editor><firstname>Reinhard</firstname><lastname>Koch</lastname></editor>
       <editor><firstname>Andreas</firstname><lastname>Kolb</lastname></editor>
       <editor><firstname>Christof</firstname><lastname>Rezk&#45;Salama</lastname></editor> 
   </authorgroup>
   <citetitle pubwork="article">Direct Image Registration without Region of Interest</citetitle>

   <publisher>
      <publishername>Eurographics Association</publishername>
   </publisher>


   <artpagenums>323-330</artpagenums> 
   <pubdate>2010</pubdate>  
   <abstract>
      <para>Standard direct image registration consists in estimating the geometric warp between a source and a target images by maximizing the photometric similarity for the pixels of a Region of Interest (RoI). The RoI must be included in the real overlap between the images otherwise standard registration algorithms fail. Determining a proper RoI is a hard &#39;chicken&#45;and&#45;egg&#39; problem since the overlap is only known after a successful registration. Almost all algorithms in the literature consider that the RoI is given. This is generally either inconvenient or unreliable.&#10;&#10;In this paper we propose a new method that registers two images without using a RoI. The key idea of our method is to consider the off&#45;target pixels as outliers. We define the off&#45;target pixels as those pixels of the source image mapped outside the target image by the current warp. We use the classical robust M&#45;estimation framework to handle both the off&#45;target pixels and the usual outliers caused&#44; for instance&#44; by occlusions. With our formulation&#44; the true image overlap is defined as the set of inliers.&#10;&#10;Experiments on synthetic and real data with the homography and Free&#45;Form Deformation show that our method outperforms standard approaches in terms of accuracy and robustness while precisely retrieving the overlap in the source and target images.
      </para>
   </abstract>
</biblioentry>
</bibliography>
