A machine learning approach to image registration verification

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

doi:10.1117/12.2663789

15 June 2023 A machine learning approach to image registration verification

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

Proceedings Volume 12526, Multimodal Image Exploitation and Learning 2023 ; 1252604 (2023) https://doi.org/10.1117/12.2663789
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Stephen DelMarco, Helen Webb, Frank Stolle, Simone Bortolami, Frank Tucker, and Travis Hillyer "A machine learning approach to image registration verification", Proc. SPIE 12526, Multimodal Image Exploitation and Learning 2023 , 1252604 (15 June 2023); https://doi.org/10.1117/12.2663789

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available