Image Processing and Machine Learning Approaches for Petrographic Thin Section Analysis

[1]  Martin Vad Bennetzen,et al.  Automatic High-Throughput Detection of Fluid Inclusions in Thin-Section Images using a Novel Algorithm , 2014 .

[2]  Andreas Günther,et al.  Semi-automatic segmentation of petrographic thin section images using a "seeded-region growing algorithm" with an application to characterize wheathered subarkose sandstone , 2015, Comput. Geosci..

[3]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[4]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[5]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  I. Varfolomeev,et al.  Integrated Study of Thin Sections: Optical Petrography and Electron Microscopy (Russian) , 2016 .

[7]  I. Jolliffe.  Principal Component Analysis , 2002 .

[8]  B. Obara.  Developing of the Image Segmentation Methods to Rock Microcracks Analysis , 2005 .

[9]  Javad Ghiasi-Freez,et al.  Semi-automated porosity identification from thin section images using image analysis and intelligent discriminant classifiers , 2012, Comput. Geosci..

[10]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[11]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[12]  W. Al-Bazzaz,et al.  Porosity, Permeability, and MHR Calculations Using SEM and Thin-section Images for Characterizing Complex Mauddud-Burgan Carbonate Reservoir , 2007 .

[13]  Pongga Dikdya Wardaya,et al.  Integrating Digital Image Processing and Artificial Neural Network for Estimating Porosity from Thin Section , 2013 .