Eigendecomposition-based Pose Detection in the Presence of Occlusion
Eigendecomposition-based Pose Detection in the Presence of Occlusion
C-Y. Chang, A. A. Maciejewski and V. Balakrishnan
In Proc. IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)
pages 569-576, Maui, HI, Oct. 29 - Nov. 3, 2001
Abstract:
Abstract Eigendecomposition-based techniques are popular for a number
of computer vision problems, e.g., object and pose detection, because
they are purely appearance-based and they require few on-line
computations. Unfortunately, they also typically require an
unobstructed view of the object whose pose is being detected. The
presence of occlusion precludes the use of the normalizations that
are typically applied and significantly alters the appearance of the
object under detection. This work presents an algorithm that is based
on applying eigen-decomposition to a quadtree representation of the
image dataset used to describe the appearance of an object. This
allows decisions concerning the pose of an object to be based on only
those portions of the image in which the algorithm has determined that
the object is not occluded. The accuracy and computational efficiency
of the proposed approach is evaluated on sixteen different objects
with up to 50% of the object being occluded.
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