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Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Estimation.


Rosenhahn,  Bodo
Computer Graphics, MPI for Informatics, Max Planck Society;

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Rosenhahn, B., Brox, T., & Weickert, J. (2005). Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Estimation. In Pattern recognition: 27th DAGM Symposium (pp. 109-116). Berlin, Germany: Springer.

This paper presents the integration of 3D shape knowledge into a variational model for level set based image segmentation and tracking. Having a 3D surface model of an object that is visible in the image of a calibrated camera, the object contour stemming from the segmentation is applied to estimate the 3D pose parameters, whereas the object model projected to the image plane helps in a top-down manner to improve the extraction of the contour and the region statistics. The present approach clearly states all model assumptions in a single energy functional. This keeps the model manageable and allows further extensions for the future. While common alternative segmentation approaches that integrate 2D shape knowledge face the problem that an object can look very different from various viewpoints, a 3D free form model ensures that for each view the model can perfectly fit the data in the image. Moreover, one solves the higher level problem of determining the object pose including its distance to the camera. Experiments demonstrate the performance of the method.