Eigendecomposition-based pose detection in the presence of occlusion Chang, Chu-Yin ; Maciejewski, Anthony A. ; Balakrishnan, Venkataramanan ; Roberts, Rodney G. "This work was supported by the Sze Tsao Chang Memorial Engineering Fund, the Office of Naval Research under contract no. N00014-97-1-0640, and the National Imagery and Mapping Agency under contract no. NMA201-00-1-1003." 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 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 sixteen different objects with up to 50% of the object being occluded. Colorado State University. Libraries 2001 text ; image application/pdf ECEaam00090.pdf FACFECEN100090ARTI eng c2001 IEEE
Eigendecomposition-based pose detection in the presence of occlusion
Chang, Chu-Yin ; Maciejewski, Anthony A. ; Balakrishnan, Venkataramanan ; Roberts, Rodney G.
"This work was supported by the Sze Tsao Chang Memorial Engineering Fund, the Office of Naval Research under contract no. N00014-97-1-0640, and the National Imagery and Mapping Agency under contract no. NMA201-00-1-1003."
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 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 sixteen different objects with up to 50% of the object being occluded.
Colorado State University. Libraries
2001
text ; image
application/pdf
ECEaam00090.pdf
FACFECEN100090ARTI
eng
c2001 IEEE