We advance new active object recognition algorithms that classify rigid objects and estimate their pose from intensity images. Our algorithms automatically detect if the class or pose of an object is ambiguous in a given image, reposition the sensor as needed, and incorporate data from multiple object views in determining the final object class and pose estimate. A probabilistic feature space trajectory (FST) in a global eigenspace is used to represent 3D distorted views of an object and to estimate the class and pose of an input object. Confidence measures for the class and pose estimates, derived using the probabilistic FST object representation, determine when additional observations are required as well as where the sensor should be positioned to provide the most useful information. We demonstrate the ability to use FSTs constructed from images rendered from computer-aided design models to recognize real objects in real images and present test results for a set of metal machined parts.

We present new test results for our active object recognition algorithms which are based on the feature space trajectory (FST) representation of objects and a neural network processor for computation of distances in global feature space. The algorithms are used to classify, and estimate the pose of objects in different stable rest positions and automatically re-position the camera if the class or pose of an object is ambiguous in a given image. Multiple object views are used in determining both the final object class and pose estimate. An FST in eigenspace is used to represent 3D distorted views of an object. FSTs are constructed using images rendered from solid models. The FSTs are analyzed to determine the camera positions that best resolve ambiguities in class or pose. Real objects are then recognized from intensity images using the FST representations derived from rendered imagery.