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      <div class="TdmEntry">Research Program<ul><li class="tdmActPage"><a href="uid6.html&#10;&#9;&#9;  ">Shape Acquisition</a></li><li><a href="uid7.html&#10;&#9;&#9;  ">Bayesian Inference</a></li><li><a href="uid8.html&#10;&#9;&#9;  ">Spectral Geometry</a></li><li><a href="uid9.html&#10;&#9;&#9;  ">Surface Deformation</a></li><li><a href="uid10.html&#10;&#9;&#9;  ">Manifold Learning</a></li></ul></div>
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	    2013</a> | <a href="http://www.inria.fr/en/teams/morpheo">Presentation of the Team MORPHEO</a> | <a href="http://morpheo.inrialpes.fr/">MORPHEO Web Site
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        <h2>Section: 
      Research Program</h2>
        <h3 class="titre3">Shape Acquisition</h3>
        <p>Recovering shapes from images is a fundamental task in computer
vision. Applications are numerous and include, in particular, 3D
modeling applications and mixed reality applications where real shapes
are mixed with virtual environments. The problem faced here is to
recover shape information such as surfaces from image information. A tremendous research
effort has been made in the past to solve this problem in the static case and a number of
solutions had been proposed. However, a fundamental issue
still to be addressed is the recovery of full shape models with possibly evolving topologies using time sequence information. The main difficulties are precision, robustness of computed shapes as well as consistency of these models over time. Additional difficulties include the integration of multi-modality sensors as well as real-time applications.</p>
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