Deep learning for 2D passive source detection in presence of complex cargo

DOI10.1088/1361-6420/abb51dzbMath1451.81400arXiv2003.04089OpenAlexW3101211554MaRDI QIDQ5132255

W. Baines, Jean C. Ragusa, Peter A. Kuchment

Publication date: 10 November 2020

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2003.04089

zbMATH Keywords

deep learningsource detectionCompton cameraillicit nuclear material

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Biomedical imaging and signal processing (92C55) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Quantum optics (81V80) (2)-body potential quantum scattering theory (81U05) Mesh generation, refinement, and adaptive methods for boundary value problems involving PDEs (65N50) Computational issues in computer and robotic vision (65D19)

Related Items (1)

Foreword to special issue of Inverse Problems on modern challenges in imaging

Uses Software

• SciPy
• TensorFlow
• Keras
• Adam
• GitHub

Cites Work

• Detecting small low emission radiating sources
• The Mathematics of Computerized Tomography
• A Bayesian approach to the detection of small low emission sources
• Compton camera imaging and the cone transform: a brief overview
• Unnamed Item

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