Dr. Simone Bortolami Profile

Dr. Simone Bortolami

at BAE Systems

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Proceedings Article | 15 June 2023 Presentation + Paper

A machine learning approach to image registration verification

Stephen DelMarco, Helen Webb, Frank Stolle, Simone Bortolami, Frank Tucker, Travis Hillyer

Proceedings Volume 12526, 1252604 (2023) https://doi.org/10.1117/12.2663789

KEYWORDS: Image registration, Machine learning, Radon transform, Image processing, Image fusion, Education and training, Detection and tracking algorithms, Windows, Thermography, Optical alignment

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Image registration is an important pre-processing step for many image exploitation algorithms such as geo-location, object recognition, vision-aided navigation, and image fusion. The utility and effectiveness of downstream exploitation algorithms depends on reliable image registration. Image registration failure can corrupt processing and performance of downstream algorithms by mis-associating image features. For example, feature mis-association leads to generation of incorrect target geo-coordinates in aerial surveillance applications, or erroneous vision-based measurements in vision aided navigation. Accurate and dependable registration failure detection mitigates deleterious effects of erroneous registration. However, in autonomous operation modes, with no human in-the-loop, and in the absence of registration solution groundtruth knowledge, verifying registration solutions is problematic. In this paper we present a machine learning based image registration verification system that operates autonomously, without ground-truth. We train a machine learning algorithm to identify correct registration solutions, even for difficult multi-modal image registration in which sensor phenomenology differences produce different feature manifestation. The verification approach includes techniques for mitigation of falsealarms that may arise due to feature ambiguity. We present examples of feature ambiguity for correlation-related registration techniques. We describe Radon transform processing, covariance estimation, and fusion techniques for feature ambiguity detection. We present numerical verification performance results from a small pilot study designed to investigate the feasibility of using machine learning for reliable registration verification.

Proceedings Article | 27 May 2022 Presentation + Paper

Optimal vision aided navigation in GPS-denied regions

Stephen Del Marco, Simone Bortolami, Victor Tom

Proceedings Volume 12100, 121000A (2022) https://doi.org/10.1117/12.2618058

KEYWORDS: Image registration, Image segmentation, Cameras, Visual process modeling, Global Positioning System, Data modeling, Unmanned aerial vehicles, Optimization (mathematics), Algorithm development

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The US Department of Defense has a need to successfully navigate in operational regions where GPS is degraded or denied. When GPS is denied, navigation of aerial platforms, including manned and unmanned aerial vehicles (UAVs), for Intelligence, Surveillance, and Reconnaissance (ISR) missions, targeting missions, or autonomous cargo delivery missions, becomes compromised. In the absence of GPS, navigating from pure inertial solutions leads to rapidly growing position errors due to drift in the inertial measurement unit. Vision Aided Navigation (VAN) approaches can aid the inertial solution to reduce navigation error, but require salient and distinct scene content for image alignment. In this paper, we present an approach to optimal path planning for VAN over operational ground regions that minimizes navigation position error. The approach uses automated pre-mission visual fiducial discovery to identify regions in imagery of the fly-over area that contain unique, salient, discriminative, and stable feature content. The discovered visual fiducial regions are used to form a map of probabilities of successful VAN at each point of the gridded fly-over region. An optimal path planning algorithm uses the probability map to determine the path over the fly-over region that maximizes navigability and minimizes VAN positioning error. Constraints, such as no-fly zones and path length constraints, are incorporated into the formulation to generate a constrained optimization problem. We present the mathematical formulation of the constrained path planning optimization problem and generate numerical results demonstrating performance.

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