Abstract
We advance active computer vision algorithms for flexible manufacturing systems that classify objects and estimate their pose from intensity images. Our algorithms automatically reposition the sensor if the class or pose of an object is ambiguous in a given image and incorporate data from multiple object views in determining the final object classification. A feature space trajectory (FST) in a global eigenfeature space is used to represent 3-D distorted views of an object. Bayesian methods are used to derive the class hypothesis, pose estimate, confidence measures, and the sensor position that best resolves ambiguity. FSTs constructed using images rendered from solid models of objects are used to process real image data.
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