Determine and Map the Spatial Extent of Core Trays Using Deep Learning Approaches (CNN's and U-Net)

References

1. O.Ronneberger, P.Fischer, and T.Brox
   , U-Net: Convolutional Networks for Biomedical Image Segmentation, arXiv:1505.04597v1 [cs.CV] 18 May 2015.
   [Google Scholar]
2. K.O'shea and R.Nash
   , An introduction to Convolutional Neural Networks, arXiv:1511.08458v2 [cs.NE] 2 Dec 2015.
   [Google Scholar]
3. S. O.Salumaa
   , Convolutional Neural Networks for Cellular Segmentation, Master's thesis University of Tartu.
   [Google Scholar]
4. S. C.Wong, A.Gatt, V.Stamatescu, and M. D.McDonnell
   , Understanding data augmentation for classification: when to warp? CoRR, abs/1609.08764, 2016.
   [Google Scholar]
5. Y.Xu, R.Jia, L.Mou, G.Li, Y.Chen, Y.Lu, and Z.Jin
   , Improved relation classification by deep recurrent neural networks with data augmentation. CoRR, abs/1601.03651, 2016.
   [Google Scholar]
6. Y.LeCun, Y.Bengio
   , The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, USA (1998) 255–258.
7. Keras
   : The Python Deep Learning library, [Online]. Available: https://keras.io/. [Accessed 14 05 2018].
   [Google Scholar]
8. Splash of Color
   : Instance Segmentation with Mask R-CNN and TensorFlow, [Online]. Available: https://engineering.matterport.com/splash-ofcolor-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46. [Accessed 21 05 2018].
   [Google Scholar]
9. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow, Matterport, [Online]. Available: https://github.com/matterport/Mask_RCNN. [Accessed 14 05 2018].
   [Google Scholar]