Augmented reality data generation for training deep learning neural network

Kevin Payumo; Alexander Huyen; Landan Seguin; Thomas T. Lu; Edward Chow; Gil Torres

doi:10.1117/12.2305202

30 April 2018 Augmented reality data generation for training deep learning neural network

Kevin Payumo, Alexander Huyen, Landan Seguin, Thomas T. Lu, Edward Chow, Gil Torres

Proceedings Volume 10649, Pattern Recognition and Tracking XXIX; 106490U (2018) https://doi.org/10.1117/12.2305202
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

One of the major challenges in deep learning is retrieving sufficiently large labeled training datasets, which can become expensive and time consuming to collect. A unique approach to training segmentation is to use Deep Neural Network (DNN) models with a minimal amount of initial labeled training samples. The procedure involves creating synthetic data and using image registration to calculate affine transformations to apply to the synthetic data. The method takes a small dataset and generates a highquality augmented reality synthetic dataset with strong variance while maintaining consistency with real cases. Results illustrate segmentation improvements in various target features and increased average target confidence.

Conference Presentation

Citation Download Citation

Kevin Payumo, Alexander Huyen, Landan Seguin, Thomas T. Lu, Edward Chow, and Gil Torres "Augmented reality data generation for training deep learning neural network", Proc. SPIE 10649, Pattern Recognition and Tracking XXIX, 106490U (30 April 2018); https://doi.org/10.1117/12.2305202

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