Showing papers by "Jeffrey H. Shapiro published in 1999"

Bounds on the area under the ROC curve

Abstract: Upper and lower bounds are derived for the area under the receiver-operating-characteristic (ROC) curve of binary hypothesis testing. These results are compared with the area-under-the-curve (AUC) approximation and the AUC lower bound recently reported by Barrett et al. [J. Opt. Soc. Am A15, 1520 (1998)].

Model-based object recognition using laser radar range imagery

Abstract: The combined effects of laser speckle and local oscillator shot noise degrade coherent laser radar range measurements. As a result, laser radar range imagery suffers from both uniformly-distributed range anomalies and Gaussian-distributed local range errors. Our goal in this research is to develop a target recognition system capable of recognizing military vehicles in range images provided by airborne laser radars. In particular, we will focus on using laser radar range imagery in a statistical model-based object recognition system. We shall present performance results for our object recognition system using laser radar data from the MIT Lincoln Laboratory Infrared Airborne Radar (IRAR) data release together with 3-D CAD models which account for the possible military targets that may be present on the site imaged by the laser radar.

Multispectral sensor fusion for ground-based target orientation estimation : FLIR, LADAR, HRR

Abstract: In our earlier work, we focused on pose estimation of ground- based targets as viewed via forward-looking passive infrared (FLIR) systems and laser radar (LADAR) imaging sensors. In this paper, we will study individual and joint sensor performance to provide a more complete understanding of our sensor suite. We will also study the addition of a high range- resolution radar (HRR). Data from these three sensors are simulated using CAD models for the targets of interest in conjunction with XPATCH range radar simulation software, Silicon Graphics workstations and the PRISM infrared simulation package. Using a Lie Group representation of the orientation space and a Bayesian estimation framework, we quantitatively examine both pose-dependent variations in performance, and the relative performance of the aforementioned sensors via mean squared error analysis. Using the Hilbert-Schmidt norm as an error metric, the minimum mean squared error (MMSE) estimator is reviewed and mean squared error (MSE) performance analysis is presented. Results of simulations are presented and discussed. In our simulations, FLIR and HRR sensitivities were characterized by their respective signal-to-noise ratios (SNRs) and the LADAR by its carrier-to-noise ratio (CNR). These figures-of-merit can, in turn, be related to the sensor, atmosphere, and target parameters for scenarios of interest.

Performance analysis for ground-based target orientation estimation: FLIR/LADAR sensor fusion

Abstract: Recognizing 3-D objects from imaging sensors has received considerable attention in the last few years. Target recognition inherently depends on target pose estimation, because target signatures vary greatly with pose even for a single target/sensor combination. This paper addresses pose estimation for ground-based targets viewed with a combination of active and passive imagers, specifically a laser radar (LADAR) range imager and a forward-looking infrared (FLIR) thermal imager. The objective is to develop Cramer-Rao type bounds for the mean-squared error which explicitly reveal the roles of sensor and scenario parameters, and permit quantitative assessment of the benefits of sensor fusion. These analytical results are compared with simulation results obtained using the Hilbert-Schmidt norm as the performance measure.