Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images
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Dursun, 2014, Recent developments in advanced aircraft aluminium alloys, Mater. Des., 56, 862, 10.1016/j.matdes.2013.12.002
Hu, 2017, Grain boundary stability governs hardening and softening in extremely fine nanograined metals, Science, 355, 1292, 10.1126/science.aal5166
Sonka, M., Hlavac, V., and Boyle, R. (2014). Image Processing, Analysis, and Machine Vision, Cengage Learning. [4th ed.].
Lewis, 2014, Future Directions in 3D Materials Science: Outlook from the First International Conference on 3D Materials Science, JOM, 66, 670, 10.1007/s11837-014-0883-5
Almsick, M.V. (2017). Microscope Image Processing, Elsevier.
Hong, 2009, Formation mechanism of the discontinuous dendrite structure in Al-La alloys, J. Univ. Sci. Technol. Beijing, 31, 1132
Stella, 2010, Characterization of the complete fiber network topology of planar fibrous tissues and scaffolds, Biomaterials, 31, 5345, 10.1016/j.biomaterials.2010.03.052
Vala, 2013, A review on Otsu image segmentation algorithm, Int. J. Adv. Res. Comput. Eng. Technol., 2, 387
Dewan, 2011, Tracking biological cells in time-lapse microscopy: An adaptive technique combining motion and topological features, IEEE Trans. Biomed. Eng., 58, 1637, 10.1109/TBME.2011.2109001
Meyer, 1990, Morphological segmentation, J. Vis. Commun. Image Represent., 1, 21, 10.1016/1047-3203(90)90014-M
Tarabalka, 2010, Segmentation and classification of hyperspectral images using watershed transformation, Pattern Recognit., 43, 2367, 10.1016/j.patcog.2010.01.016
Birkbeck, N., Cobzas, D., and Jagersand, M. (2009, January 7–8). An Interactive Graph Cut Method for Brain Tumor Segmentation. Proceedings of the Applications of Computer Vision (WACV), Snowbird, UT, USA.
Shi, 2000, Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 22, 888, 10.1109/34.868688
Jain, 2010, Data clustering: 50 years beyond K-means, Pattern Recognit. Lett., 31, 651, 10.1016/j.patrec.2009.09.011
Jonathan, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39, 640, 10.1109/TPAMI.2016.2572683
Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–8). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA.
Simonyan, K., and Zisserman, A. (2015, January 7–9). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the ICLR 2015, San Diego, CA, USA.
He, K., Zhang, X., and Ren, S. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.
Noh, H., Hong, S., and Han, B. (2015, January 13–16). Learning Deconvolution Network for Semantic Segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.
Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.
Liu, W., Rabinovich, A., and Berg, A.C. (arXiv, 2015). Parsenet: Looking wider to see better, arXiv.
Yu, F., and Koltun, V. (arXiv, 2015). Multi-scale context aggregation by dilated convolutions, arXiv.
Chen, 2018, DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, IEEE Trans. Pattern Anal. Mach. Intell., 40, 834, 10.1109/TPAMI.2017.2699184
Everingham, 2015, The pascal visual object classes challenge: A retrospective, Int. J. Comput. Vis., 111, 98, 10.1007/s11263-014-0733-5
Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., and Yuille, A. (2016, January 27–29). Detect What you Can: Detecting and Representing Objects Using Holistic Models and Body Parts. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.
Alatan, 1998, Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework, IEEE Trans. Circuits Syst. Video Technol., 8, 802, 10.1109/76.735378
Doulamis, 2000, Efficient unsupervised content-based segmentation in stereoscopic video sequences, Int. J. Artif. Intell. Tools, 9, 277, 10.1142/S0218213000000197
Feng, 2017, Reconstruction of three-dimensional grain structure in polycrystalline iron via an interactive segmentation method, Int. J. Miner. Metall. Mater., 24, 257, 10.1007/s12613-017-1403-8
Waggoner, 2013, 3D Materials image segmentation by 2D propagation: A graph-cut approach considering homomorphism, IEEE Trans. Image Process., 22, 5282, 10.1109/TIP.2013.2284071
(2018, January 08). Python Language Reference. Available online: http://www.python.org.
(2018, January 08). Tensorflow. Available online: http://www.tensorfly.cn/.
Laganière, R. (2017). OpenCV 3 Computer Vision Application Programming Cookbook, Packt Publishing Ltd. [3rd ed.].
Glorot, X., and Bengio, Y. (2010, January 13–15). Understanding the Difficulty of Training Deep Feedforward Neural Networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Sardinia, Italy.
Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the International Conference on Machine Learning, Lille, France.
(2018, January 08). Geforce GTX 1080Ti. Available online: https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080-ti/.