Feature Space Trajectory Representation for Active Vision

A new feature space trajectory (FST) description of 3-D distorted views of an object is advanced for active vision applications. In an FST, di erent distorted object views are vertices in feature space. A new eigen-feature space and Fourier transform features are used. Vertices for di erent adjacent distorted views are connected by straight lines so that an FST is created as the viewpoint changes. Each di erent object is represented by a distinct FST. An object to be recognized is represented as a point in feature space; the closest FST denotes the class of the object, and the closest line segment on the FST indicates its pose. A new neural network is used to e ciently calculate distances. We discuss its uses in active vision. Apart from an initial estimate of object class and pose, the FST processor can specify where to move the sensor to: con rm class and pose, to grasp the object, or to focus on a speci c object part for assembly or inspection. We advance initial remarks on the number of aspect views needed and which aspect views are needed to represent an object.