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