A graphical user interface for multi-modal operating condition sampling

Charles Hill; Adam Nolan; Shaun Stephens; Patrick Bischof; Matthew Scherreik

doi:10.1117/12.2663064

13 June 2023 A graphical user interface for multi-modal operating condition sampling

Charles Hill, Adam Nolan, Shaun Stephens, Patrick Bischof, Matthew Scherreik

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290D (2023) https://doi.org/10.1117/12.2663064
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Synthetic data is often leveraged for training and testing Automatic Target Recognition (ATR) systems on a variety of operating conditions (OCs). Existing mechanisms for creating the sampling distribution of OCs to generate this data are difficult to visualize, modify, and extend. To address this, we created a user interface and toolchain for multi-modal OC sampling from probabilistic graphical models (PGMs). Our web browser-based interface allows for visualizing the PGMs, modifying the conditional probability distributions of their nodes, importing and exporting their state, operation in single- or multi-modality configurations, and persisting generated samples to a relational database. The Vue-driven web interface, programmatic interface, as well as the Python machinery for OC sampling and persistence have been containerized to allow for simplified deployment and distribution. The work described here supplies a new baseline for OC generation for single- and multi-sensor simulation and fusion.

Conference Presentation

Citation Download Citation

Charles Hill, Adam Nolan, Shaun Stephens, Patrick Bischof, and Matthew Scherreik "A graphical user interface for multi-modal operating condition sampling", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290D (13 June 2023); https://doi.org/10.1117/12.2663064

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