Mr. Travis Hilyer Profile

Travis Hilyer

at U.S. Army Combat Capabilities Development Command

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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.

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