Cloud-Based Borehole Image Interpretation Workflow Using Machine Learning

References

1. Cormen, T.H., Leiserson, C.E., Rivest, R., and Stein, C.
   [2009] Introduction to Algorithms, Third Edition. MIT Press
2. Jungmann, M., Kopal, M., Clauser, C. and Berlage, T.
   [2011] Multi-class supervised classification of electrical borehole wall images using texture features. Computers & Geosciences, 37(4), 541–553
   [Google Scholar]
3. Luthi, S.M.
   [1994] Textural Segmentation of Digital Rock Images into Bedding Units Using Texture Energy and Cluster Labels. Mathematical Geology, 26(2), 181–196
   [Google Scholar]
4. Salvador, S., and Chan, P.
   [2004] Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms. 16th IEEE International Conference on Tools with Artificial Intelligence.
   [Google Scholar]
5. Zhang, T., Gelman, A., and Laronga, R.
   [2017] Structure- and Texture-Based Fullbore Image Reconstruction. Mathematical Geoscience, 49(2), 195–215
   [Google Scholar]
6. Zujovic, J., Pappas, T.N., and Neuhoff, D.L.
   [2013] Structural Texture Similarity Metrics for Image Analysis and Retrieval. IEEE Transactions on Image Processing, 22(7), 2545 – 2558
   [Google Scholar]