International Journal of Computer Vision
View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse Representations for Image Decompositions
International Journal of Computer Vision
Subspace position measurement in the presence of occlusion
Pattern Recognition Letters
Indexing for local appearance-based recognition of planar objects
Pattern Recognition Letters
Hierarchical Organization of Appearance-Based Parts and Relations for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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Planar pose measurement from images is an important problem for automated assembly and inspection. In addition to accuracy and robustness, ease of use is very important for real world applications. Recently, Murase and Nayar have presented the "parametric eigenspace " for object recognition and pose measurement based on training images. Although their system is easy to use, it has potential problems with background clutter and partial occlusions. We present an algorithm that is robust in these terms. It uses several small features on the object rather than a monolithic template. These "eigenfeatures" are matched using a median statistic, giving the system robustness in the face of background clutter and partial occlusions. We demonstrate our algorithm's pose measurement accuracy with a controlled test, and we demonstrate its detection robustness on cluttered images with the objects of interest partially occluded.