Quadtree-based eigendecomposition for pose estimation in the presence of occlusion and background clutter
Date
2006
Authors
Saitwal, Kishor, author
Roberts, Rodney G., author
Balakrishnan, Venkataramanan, author
Maciejewski, Anthony A., author
Chang, Chu-Yin, author
Springer-Verlag London Limited, publisher
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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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Subject
partial occlusion
background clutter
singular value decomposition
object recognition
pose estimation
quadtree decomposition