Feature space trajectory representation for active vision

Michael A. Sipe; David P. Casasent; Leonard Neiberg

doi:10.1117/12.271506

4 April 1997 Feature space trajectory representation for active vision

Michael A. Sipe, David P. Casasent, Leonard Neiberg

Proceedings Volume 3077, Applications and Science of Artificial Neural Networks III; (1997) https://doi.org/10.1117/12.271506
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

A new feature space trajectory (FST) description of 3D 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 calculated 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 close and pose, to grasp the object, or to focus on a specific object part for assembly or inspection. We advanced initial remarks on the number of aspect views needed and which aspect views are needed to represent an object.

Citation Download Citation

Michael A. Sipe, David P. Casasent, and Leonard Neiberg "Feature space trajectory representation for active vision", Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); https://doi.org/10.1117/12.271506

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