Global feature space neural network for active object recognition
Michael A. Sipe, David P. Casasent · 2003
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.