Roberts, Rodney G., authorBalakrishnan, Venkataramanan, authorMaciejewski, Anthony A., authorChang, Chu-Yin, authorIEEE, publisher2007-01-032007-01-032001Chang, C-Y., et al., Eigendecomposition-Based Pose Detection in the Presence of Occlusion, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001): Expanding the Societal Role of Robotics in the Next Millennium, October 29-November 3, 2001, Maui, Hawaii: 576-569.http://hdl.handle.net/10217/1351Eigendecomposition-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.born digitalproceedings (reports)eng©2001 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.eigenvalues and eigenfunctionscomputer visioncomputational complexityobject detectionquadtreesText