<title>Feature spacing trajectory representation and processing for active vision</title>

Abstract

A new feature space trajectory (FST) description of 3-D distorted views of an object is advanced for active vision applications. In an FST, different distorted object views are vertices in feature space. A new eigen-feature space and Fourier transform features are used. Vertices for different adjacent distorted views are connected by straight lines so that an FST is created as the viewpoint changes. Each different 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 efficiently 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: confirm class and pose, to grasp the object, or to focus on a specific 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. We note the superiority of our eigenspace for discrimination, how it can provide shift invariance, and how the FST overcomes problems associated with other classifiers.

<title>Feature space trajectory representation for active vision</title>

“…A human would have difficulty making these kinds of determinations from aspect views of an object, but our algorithm has provided this information correctly and automatically. In a prior paper [20], we demonstrated an active pose estimation sequence for this coffee cup object. The second test case is the synthetic block with flange object on the right half of Fig.…”