High-precision Infantry Training System (HITS)

DelMarco, Stephen; Bortolami, Simone B.; Webb, Helen; Delva, J.G.R.; Chen, David; Tucker, Frank H.; Hillyer, Travis

doi:10.33012/2020.17741

Cited by 1 publication

(2 citation statements)

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“…We generated registration verification performance results from a small pilot study designed to investigate feasibility of the verification approach. We collected thermal imagery, and generated synthetic reference range imagery from a LIDAR point cloud model of the scene, as described in [15]. Figure 16 provides example thermal imagery from the data collection.…”

Section: Numerical Resultsmentioning

confidence: 99%

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A machine learning approach to image registration verification

DelMarco,

Webb,

Stolle

et al. 2023

Multimodal Image Exploitation and Learning 2023

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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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Section: Numerical Resultsmentioning

confidence: 99%

“…Figure 4 contains zoom-ins of registered, binarized gradient overlay imagery. The imagery consists of a thermal image and a synthetic range reference image generated from a point cloud model of the scene, as described in [15]. The red channel corresponds to the reference imagery, and the blue-green channels correspond to the thermal imagery.…”

Section: Overviewmentioning

confidence: 99%