Abstract

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 project 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.