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Proceedings Paper

Approach to reduce the computational image processing requirements for a computer vision system using sensor preprocessing and the Hotelling transform
Author(s): Thomas R. Schei; Cameron H. G. Wright; Daniel J. Pack
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Paper Abstract

We describe a new development approach to computer vision for a compact, low-power, real-time system whereby we take advantage of preprocessing in a biomimetic vision sensor and a computational strategy using subspace methods and the Hotelling transform in an effort to reduce the computational imaging load. The approach is two-pronged: 1) design the imaging sensor to reduce the computational load as much as possible up front, and 2) employ computational algorithms that efficiently complete the remaining image processing steps needed for computer vision. This strategy works best if the sensor design and the computational algorithm design evolve together as a synergistic, mutually optimized pair. Our system uses the biomimetic “fly-eye” sensor described in previous papers that offers significant preprocessing. However, the format of the image provided by the sensor is not a traditional bitmap and therefore requires innovative computational manipulations to make best use of this sensor. The remaining computational algorithms employ eigenspace object models derived from Principle Component Analysis, and the Hotelling transform to simplify the models. The combination of sensor preprocessing and the Hotelling transform provides an overall reduction in the computational imaging requirements that would allow real-time computer vision in a compact, low-power system.

Paper Details

Date Published: 11 March 2005
PDF: 12 pages
Proc. SPIE 5674, Computational Imaging III, (11 March 2005); doi: 10.1117/12.587056
Show Author Affiliations
Thomas R. Schei, Northrop Grumman Mission Systems (United States)
Cameron H. G. Wright, Univ. of Wyoming (United States)
Daniel J. Pack, U.S. Air Force Academy (United States)

Published in SPIE Proceedings Vol. 5674:
Computational Imaging III
Charles A. Bouman; Eric L. Miller, Editor(s)

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