Machine learning in/with information fusion for infrastructure understanding, panel summary

Erik Blasch; Lynne L. Grewe; Edward L. Waltz; Paul Bendich; Vladimir Pavlovic; Ivan Kadar; Chee-Yee Chong

doi:10.1117/12.2559416

26 May 2020 Machine learning in/with information fusion for infrastructure understanding, panel summary

Erik Blasch, Lynne L. Grewe, Edward L. Waltz, Paul Bendich, Vladimir Pavlovic, Ivan Kadar, Chee-Yee Chong

Proceedings Volume 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX; 114230O (2020) https://doi.org/10.1117/12.2559416
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

During the 2019 SPIE DSS conference, panelists were invited to highlight the trends and use of artificial intelligence and machine learning (AI/ML) for information fusion. The common themes between the panelists include leveraging AI/ML coordinated with Information Fusion for: (1) knowledge reasoning, (2) model building, (3) object recognition and tracking, (4) multimodal learning, and (5) information processing. The opportunity for machine learning exists within all the fusion levels of the Data Fusion Information Group model.

Conference Presentation

Citation Download Citation

Erik Blasch, Lynne L. Grewe, Edward L. Waltz, Paul Bendich, Vladimir Pavlovic, Ivan Kadar, and Chee-Yee Chong "Machine learning in/with information fusion for infrastructure understanding, panel summary", Proc. SPIE 11423, Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX, 114230O (26 May 2020); https://doi.org/10.1117/12.2559416

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