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== <big><b>Project SHARED</b> - <b>Sh</b>ape <b>A</b>nalysis and <b>Re</b>gistration of People Using <b>D</b>ynamic Data</big> ==
 
== <big><b>Project SHARED</b> - <b>Sh</b>ape <b>A</b>nalysis and <b>Re</b>gistration of People Using <b>D</b>ynamic Data</big> ==
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[[File:IMG_0002.JPG|256px]]
<b>Project SHARED</b> seeks to investigate novel shape analysis method that exploits
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[[File:IMG_0009.JPG|256px]]
large redundancy of information from dynamic or movement data.
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[[File:Fred.png|172px]]
<br />The main interest of the proposed approach is
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<br /> <br />
* to acquire and preprocess the subjects’ movement data so as to characterize anatomical or functional landmarks, and
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* to devise a registration technique that makes use of this rich set of information to guarantee reliable correspondence. Appreciably, with the recent advances in imaging technologies we now have growing accessibility to capture the shape and motion of human skin from optical motion capture systems, and of organs from medical imaging devices.  
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<b>Project SHARED</b> is a French National project funded by ANR(L'Agence Nationale de la Recherche) in the framework of "Chaires d'Excellence" program.  
* to investigate statistical analysis of deforming shapes, tightly coupling the shape identity and shape change due to movement. A statistical atlas spanning over the variations of shape identity and shape deformation will be constructed, which will be used to revise the registration module with great stability and robustness.
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* Period: 15 December 2010~ 14 December 2014 (4 years)
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* Budget: 220 000 Euros
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<!-- *First, a large enough collection of dynamic, time-varying data sets should be acquired. Appreciably, with the recent advances in imaging technologies we now have growing accessibility to capture shape and motion of people with high frequency. For example, we track the location of selected skin surfaces (markers) using a number of synchronized cameras. After post-processing of marker trajectories we obtain sequences of human body motions in the form of animated meshes.
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*Second, a shape model should be constructed. The model should be faithful to dynamic properties of deforming surfaces that are obtained through data acquisition
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*Third, we want to move towards devising new non-rigid registration and spatio-temporal segmentation techniques. We have to put entirely new perspective on the traditional registration pipeline, trying to understand where and how the mobility can be encapsulated. The dynamic features along with shape-based geometric features will be used to guide the full correspondence as well as the transformation for the optimal match.<br /><br />
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-->
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Project SHARED seeks to investigate novel shape analysis methods that exploit large redundancy of information from dynamic or movement data. Specific objectives of the project are: <br />
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{| class="wikitable"
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# Design shape acquisition and analysis method where kinematic properties can be fully learned;
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# Develop new registration techniques that uses the kinematic properties obtained above so as to put similar deformation behaviours in correspondence;
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# Develop statistical model that incorporates the dynamic shape variation and shape identity variation; Revise the registration technique with the statistical shape atlas, towards a more robust and efficient application.
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|}

Latest revision as of 16:30, 21 December 2012

Project SHARED - Shape Analysis and Registration of People Using Dynamic Data

IMG 0002.JPG IMG 0009.JPG Fred.png

Project SHARED is a French National project funded by ANR(L'Agence Nationale de la Recherche) in the framework of "Chaires d'Excellence" program.

  • Period: 15 December 2010~ 14 December 2014 (4 years)
  • Budget: 220 000 Euros


Project SHARED seeks to investigate novel shape analysis methods that exploit large redundancy of information from dynamic or movement data. Specific objectives of the project are:

  1. Design shape acquisition and analysis method where kinematic properties can be fully learned;
  2. Develop new registration techniques that uses the kinematic properties obtained above so as to put similar deformation behaviours in correspondence;
  3. Develop statistical model that incorporates the dynamic shape variation and shape identity variation; Revise the registration technique with the statistical shape atlas, towards a more robust and efficient application.