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

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

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

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