The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Image driven machine learning methods for microstructure recognition
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 43,398
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 8,947
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Cited by
Bock, F. E., Aydin, R. C., Cyron, C. J., Huber, N., Kalidindi, S. R., & Klusemann, B. (2019) · Frontiers in Materials · doi:10.3389/fmats.2019.00110 · Cited by 403
Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R. (2018) · Integrating Materials and Manufacturing Innovation · doi:10.1007/s40192-018-0117-8 · Cited by 347
Mukherjee, T., Elmer, J. W., Wei, H. L., Lienert, T. J., Zhang, W., Kou, S., & DebRoy, T. (2023) · Progress in Materials Science · doi:10.1016/j.pmatsci.2023.101153 · Cited by 267
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123
Ukwaththa, J., Herath, S., & Meddage, D. P. P. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.110294 · Cited by 118
Metadata
- Authors
- Chowdhury, A., Kautz, E., Yener, B., & Lewis, D.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 123
- Pages
- 176-187
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2462290730
- Cited by
- 326
- References
- 37
- FWCI
- 7.86
- Type
- journal article
- Language
- en
- Open access
- bronze
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- The paper
- Chowdhury, A., Kautz, E., Yener, B., & Lewis, D. (2016). Image driven machine learning methods for microstructure recognition. Computational Materials Science. https://doi.org/10.1016/j.commatsci.2016.05.034
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
