Share Email Print
cover

Proceedings Paper

Sources of uncertainty in feature-based image registration algorithms
Author(s): Paul O. Sundlie; Clark N. Taylor; Joseph A. Fernando
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

One significant technological barrier to enabling multi-sensor integrated ISR is obtaining an accurate understanding of the uncertainty present from each sensor. Once the uncertainty is known, data fusion, cross-cueing, and other exploitation algorithms can be performed. However, these algorithms depend on the availability of accurate uncertainty information from each sensor.

In many traditional systems (e.g., a GPS/IMU-based navigation system), the uncertainty values for any estimate can be derived by carefully observing or characterizing the uncertainty of its inputs and then propagating that uncertainty through the estimation system.

In this paper, we demonstrate that image registration uncertainty, on the other hand, cannot be characterized in this fashion. Much of the uncertainty in the output of a registration algorithm is due to not only the sensors used to collect the data, but also data collected and the algorithms used. In this paper, we present results of an analysis of feature-based image registration uncertainty. We make use of Monte Carlo analysis to investigate the errors present in an image registration algorithm. We demonstrate that the classical methods of propagating uncertainty from the inputs to the outputs yields significant under-estimates of the true uncertainty on the output. We then describe at least two possible sources of additional error present in feature-based methods and demonstrate the importance of these sources of error.

Paper Details

Date Published: 20 May 2015
PDF: 10 pages
Proc. SPIE 9464, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI, 94640Z (20 May 2015); doi: 10.1117/12.2180628
Show Author Affiliations
Paul O. Sundlie, Univ. of Dayton Research Institute (United States)
Clark N. Taylor, U.S. Air Force Research Lab. (United States)
Joseph A. Fernando, Univ. of Dayton Research Institute (United States)


Published in SPIE Proceedings Vol. 9464:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VI
Tien Pham; Michael A. Kolodny, Editor(s)

© SPIE. Terms of Use
Back to Top