Object representation is fundamental to Automated Target Recognition (ATR). Many ATR approaches choose a basis, such as a wavelet or Fourier basis, to represent the target. Recently, advancements in Image and Signal processing have shown that object recognition can be improved if, rather than a assuming a basis, a database of training examples is used to learn a representation. We discuss learning representations using Non-parametric Bayesian topic models, and demonstrate how to integrate information from other sources to improve ATR. We apply the method to EO and IR information integration for vehicle target identification and show that the learned representation of the joint EO and IR information improves target identification by 4%. Furthermore, we demonstrate that we can integrate text and imagery data to direct the representation for mission specific tasks and improve performance by 8%. Finally, we illustrate integrating graphical models into representation learning to improve performance by 2%.
A canonical problem for autonomy is search and discovery. Often, searching needs to be unpredictable in order
to be effective. In this paper, we investigate and compare the effectiveness of the traditional and predictable
lawnmower search strategy to that of a random search. Specifically the family of searches with paths determined
by heavy tailed distributions called Lévy stable searches is investigated. These searches are characterized by long
flight paths, followed by a new random direction, with the flight path lengths determined by the distribution
parameter α. Two basic search scenarios are considered in this study: stationary targets, and moving targets,
both on planar surfaces. Monte-Carlo simulations demonstrate the advantages of Lévy over the lawnmower
strategy especially for moving targets. Ultimately to corroborate the suitability of the Lévy strategy for UAVs,
we implement and demonstrate the feasibility of the algorithm in the Multiple Unified Simulation Environment
(MUSE), which includes vehicle's constraints and dynamics. The MUSE / Air Force Synthetic Environment for
Reconnaissance and Surveillance (AFSERS) simulation system is the primary virtual ISR and UAV simulation
within DOD for command and staff level training for the Joint Services.
Edge features are often used in computer vision for image exploitation algorithms. A method to extract edge
features that is robust to contrast change, translation, rotation, noise and scale change is presented. This method
consists of the following steps: decompose the image into it's level set shapes, smooth the shapes, locate sections
of the shape borders that have nearly constant curvature, and locate a key point based on these curve sections.
The level sets are found using the Fast Level Set Transform (FLST). An affine invariant smoothing technique was
then applied to the level set shape borders to reduce pixel effects and noise, and an intrinsic scale was estimated
from the level set borders. The final step was key point location and scale estimation using the Helmholtz
principle. These key points were found to be more resilient to large scale changes than the SIFT key points.