Quadtree-based eigendecomposition for pose detection in the presence
of occlusion and background clutter
Quadtree-based eigendecomposition for pose detection in the presence
of occlusion and background clutter
C-Y. Chang, A. A. Maciejewski, V. Balakrishnan, R. G. Roberts, and K. Saitwal
In Pattern Analysis and Applications
, Vol. 10, No. 1, pp. 15-31, Feb. 2007
Abstract:
Abstract Eigendecomposition-based techniques are
popular for a number of computer vision problems,
e.g., object and pose estimation, 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 and
background clutter 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
eigendecomposition 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 16 different objects with up to 50% of the object
being occluded and on images of ships in a dockyard.
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