Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels

[1]  Elizabeth A. Holm,et al.  High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel , 2018, Microscopy and Microanalysis.

[2]  Jian Yang,et al.  Importance-Aware Semantic Segmentation for Autonomous Vehicles , 2019, IEEE Transactions on Intelligent Transportation Systems.

[3]  Yuanyuan Zhu,et al.  Towards bend-contour-free dislocation imaging via diffraction contrast STEM. , 2018, Ultramicroscopy.

[4]  Wei Li,et al.  Automated defect analysis in electron microscopic images , 2018, npj Computational Materials.

[5]  Ole Winther,et al.  A Deep Learning Approach to Identify Local Structures in Atomic‐Resolution Transmission Electron Microscopy Images , 2018, Advanced Theory and Simulations.

[6]  Julien Cohen-Adad,et al.  AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks , 2017, Scientific Reports.

[7]  Mario Fritz,et al.  Advanced Steel Microstructural Classification by Deep Learning Methods , 2017, Scientific Reports.

[8]  Yu Liu,et al.  A review of semantic segmentation using deep neural networks , 2017, International Journal of Multimedia Information Retrieval.

[9]  Yepeng Guan,et al.  Image Enhancement via Subimage Histogram Equalization Based on Mean and Variance , 2017, Comput. Intell. Neurosci..

[10]  Sergei V. Kalinin,et al.  Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. , 2017, ACS nano.

[11]  R. Asahi,et al.  Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics , 2017 .

[12]  Mrugendrasinh Rahevar,et al.  Survey on semantic image segmentation techniques , 2017, 2017 International Conference on Intelligent Sustainable Systems (ICISS).

[13]  Yoshua Bengio,et al.  The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[14]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  I. M. Robertson,et al.  Enhancing radiation tolerance by controlling defect mobility and migration pathways in multicomponent single-phase alloys , 2016, Nature Communications.

[16]  Jian Wang,et al.  Disconnections and other defects associated with twin interfaces , 2016 .

[17]  Bülent Yener,et al.  Image driven machine learning methods for microstructure recognition , 2016 .

[18]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[19]  Jianfei Cai,et al.  Beyond pixels: A comprehensive survey from bottom-up to semantic image segmentation and cosegmentation , 2015, J. Vis. Commun. Image Represent..

[20]  Elizabeth A. Holm,et al.  A computer vision approach for automated analysis and classification of microstructural image data , 2015 .

[21]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[22]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[23]  Arthur F. Voter,et al.  The relationship between grain boundary structure, defect mobility, and grain boundary sink efficiency , 2015, Scientific Reports.

[24]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[26]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[27]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[28]  Jürgen Schmidhuber,et al.  Steel defect classification with Max-Pooling Convolutional Neural Networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[29]  Jie Zhao,et al.  A Method for Detection and Classification of Glass Defects in Low Resolution Images , 2011, 2011 Sixth International Conference on Image and Graphics.

[30]  M. Graef,et al.  Diffraction contrast STEM of dislocations: imaging and simulations. , 2011, Ultramicroscopy.

[31]  R. Andrievski BEHAVIOR OF RADIATION DEFECTS IN NANOMATERIALS , 2011 .

[32]  Ramana M. Pidaparti,et al.  Classification of corrosion defects in NiAl bronze through image analysis , 2010 .

[33]  Sungtae Kim,et al.  Phase stability and structural defects in high-temperature Mo–Si–B alloys , 2008 .

[34]  W. Sigle ANALYTICAL TRANSMISSION ELECTRON MICROSCOPY , 2005 .

[35]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[36]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[37]  D. Clery,et al.  Control and Use of Defects in Materials , 1998, Science.

[38]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[39]  P. Lin,et al.  High-resolution study of ferroelectric domain boundaries in lithium tantalate , 1982 .

[40]  D. Joy,et al.  The formation and interpretation of defect images from crystalline materials in a scanning transmission electron microscope. , 1976, Ultramicroscopy.

[41]  M. Makin,et al.  DISLOCATION MOVEMENT THROUGH RANDOM ARRAYS OF OBSTACLES , 1966 .

[42]  R. Ham,et al.  A systematic error in the determination of dislocation densities in thin films , 1961 .

[43]  M. Whelan,et al.  A kinematical theory of diffraction contrast of electron transmission microscope images of dislocations and other defects , 1960, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.