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Clustered stochastic optimization for object recognition and pose estimation

Gall, J├╝rgen and Rosenhahn, Bodo and Seidel, Hans-Peter

MPI-I-2007-4-001. April 2007, 23 pages. | Status: available - back from printing | Next --> Entry | Previous <-- Entry

Abstract in LaTeX format:
We present an approach for estimating the 3D position and in case of
articulated objects also the joint configuration from segmented 2D
images. The pose estimation without initial information is a challenging
optimization problem in a high dimensional space and is essential for
texture acquisition and initialization of model-based tracking
algorithms. Our method is able to recognize the correct object in the
case of multiple objects and estimates its pose with a high accuracy.
The key component is a particle-based global optimization method that
converges to the global minimum similar to simulated annealing. After
detecting potential bounded subsets of the search space, the particles
are divided into clusters and migrate to the most attractive cluster as
the time increases. The performance of our approach is verified by means
of real scenes and a quantative error analysis for image distortions.
Our experiments include rigid bodies and full human bodies.
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  AUTHOR = {Gall, J{\"u}rgen and Rosenhahn, Bodo and Seidel, Hans-Peter},
  TITLE = {Clustered stochastic optimization for object recognition and pose estimation},
  TYPE = {Research Report},
  INSTITUTION = {Max-Planck-Institut f{\"u}r Informatik},
  ADDRESS = {Stuhlsatzenhausweg 85, 66123 Saarbr{\"u}cken, Germany},
  NUMBER = {MPI-I-2007-4-001},
  MONTH = {April},
  YEAR = {2007},
  ISSN = {0946-011X},