Feb/March_AMP_Digital
A D V A N C E D M A T E R I A L S & P R O C E S S E S | F E B R U A R Y / M A R C H 2 0 2 1 1 8 structure Characterization, CRC Press, p 241-258, 2019. 5. B.L. DeCost, et al., Computer Vision and Machine Learning for Autonomous Characterization of AM Powder Feedstocks, JOM, 69, p 456-465, 2017. 6. A.R. Kitahara and E.A. Holm, Microstructure Cluster Analysis with Transfer Learning and Unsupervised Learning, Integrated Materials and Manufacturing Innovation, 7, p 148-156, 2018. 7. J. Ling, et al., Building Data-driven Models with Microstructural Images: Generalization and Interpretability, Materials Discovery, 10, p 19-28, 2017. 8. R. Szeliski, Computer Vision: Algorithms and Applications, Springer, New York, 2010. 9. P. Flach, Machine Learning: The Art and Science of Algorithms that Make Sense of Data, Cambridge University Press, 2012. 10. Y. LeCun, Y. Bengio, and G. Hinton, Deep Learning, Nature, 521, p 436-444, 2015. 11. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. 12. G. Csurka, et al., Visual Cate- gorization with Bags of Keypoints, Workshop on Statistical Learning in Computer Vision, ECCV 1, p 1-16, 2014. 13. K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-scale Image Recognition, International Conference on Learning Representations, arXiv:1409.1556, p 1-14, 2015. 14. O. Russakovsky, et al., Imagenet Large Scale Visual Recognition Chal- lenge, International Journal of Computer Vision, 115, p 211-252, 2015. 15. T.M. Mitchell, Machine Learning, McGraw-Hill, 1997. 16. S. Lloyd, Least Squares Quantiza- tion in PCM, IEEE Transactions on Infor- mation Theory, 28, p 129-137, 1982. 17. C. Shorten and T.M. Khoshgoftaar, A survey on Image Data Augmentation for Deep Learning, Journal of Big Data, 6, p 60, 2019. 18. B.L. DeCost, et al., UHCSDB: UltraHigh Carbon Steel Micrograph DataBase, Integrated Materials and Manufacturing Innovation, 6, p 197-205, 2017. 19. C.A. Schneider, W.S. Rasband, and K.W. Eliceiri, NIH Image to ImageJ: 25 Years of Image Analysis, Nature Methods, 9, p 671-675, 2012. 20. A. Bansal, et al., PixelNet: Rep- resentation of the Pixels, by the Pixels, and for the Pixels, arXiv:1702.06506 [cs.CV ], p 1-17, 2016. 21. T. Stan, Z. Thompson, and P. Voorhees, Optimizing Convolutional Neural Networks to Perform Semantic Segmentation on Large Materials Imaging Datasets: X-ray Tomography and Serial Sectioning, Materials Characterization, 160, 110119, 2020. 22. L. Liu, et al., Deep Learning for Generic Object Detection: A Survey, International Journal of Computer Vision, 128, p 261-318, 2020.
Made with FlippingBook
RkJQdWJsaXNoZXIy MjA4MTAy