1 |
Hwang, S.-W., Tazuru, S., Sugiyama, J. 2020. Wood identification of historical architecture in Korea by Synchrotron X-ray microtomography-based three-dimensional microstructural imaging. Journal of the Korean Wood Science and Technology 48(3): 283-290.
DOI
|
2 |
Sugiarto, B., Prakasa, E., Wardoyo, R., Damayanti, R., Dewi, L.M., Pardede, H.F., Rianto, Y. 2017. Wood identification based on histogram of oriented gradient (HOG) feature and support vector machine (SVM) classifier. In: 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), pp. 337-341.
|
3 |
Yang, S.Y., Lee, H.G., Park, Y., Chung, H., Kim, H., Park, S.Y., Yeo, H. 2019. Wood species classification utilizing ensembles of convolutional neural networks established by near-infrared spectra and images acquired from Korean softwood lumber. Journal of the Korean Wood Science and Technology 47(4): 385-392.
DOI
|
4 |
Yu, S., Jia, S., Xu, C. 2017. Convolutional neural networks for hyperspectral image classification. Neurocomputing 219: 88-98.
DOI
|
5 |
He, K., Zhang, X., Ren, S., Sun, J. 2016. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778.
|
6 |
Hadiwidjaja, M.L., Gunawan, P.H., Prakasa, E., Rianto, Y., Sugiarto, B., Wardoyo, R., Damaryati, R., Sugiyarto, K., Dewi, L.M., Astutiputri, V.F. 2019. Developing wood identification system by local binary pattern and hough transform method. Journal of Physics: Conference Series 1192(1): 012053.
DOI
|
7 |
Seth, W. 2019. Deep Learning from Scratch. O'Reilly Media.
|
8 |
Chollet, F. 2017. Xception: Deep Learning with Depthwise Separable Convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251-1258.
|
9 |
Fukushima, K. 1980. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36: 193-202.
DOI
|
10 |
Geron, A. 2019. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media.
|
11 |
Howard, A.G., Zhu, M.C., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. ArXiv Preprint ArXiv: 1704.04861.
|
12 |
Hussain, M., Bird, J.J., Faria, D.R. 2018. A Study on Cnn Transfer Learning for Image Classification. In: Lotfi A., Bouchachia H., Gegov A., Langensiepen C., McGinnity M. (eds) Advances in Computational Intelligence Systems, pp. 191-202.
|
13 |
Marmanis, D., Datcu, M., Esch, T., Stilla, U. 2015. Deep learning earth observation classification using ImageNet pretrained networks. IEEE Geoscience and Remote Sensing Letters 13(1): 105-109.
DOI
|
14 |
Jeon, W.S., Lee, H.M., Park, J.H. 2020. Comparison of anatomical characteristics for wood damaged by oak wilt and sound wood from quercus mongolica. Journal of the Korean Wood Science and Technology 48(6): 807-819.
DOI
|
15 |
Kobayashi, K., Kegasa, T., Hwang, S.W., Sugiyama, J. 2019. Anatomical features of Fagaceae wood statistically extracted by computer vision approaches: Some relationships with evolution. PloS One 14(8): e0220762.
DOI
|
16 |
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q. 2017. Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700-4708.
|
17 |
Jeon, W.S., Kim, Y.K., Lee, J.A., Kim, A.R., Darsan, B., Chung, W.Y., Kim, N.H. 2018. Anatomical characteristics of three Korean bamboo species. Journal of the Korean Wood Science and Technology 46(1): 29-37.
DOI
|
18 |
Kwon, O., Lee, H.G., Lee, M.R., Jang, S., Yang, S.Y., Park, S.Y., Yeo, H. 2017. Automatic wood species identification of Korean softwood based on convolutional neural networks. Journal of the Korean Wood Science and Technology 45(6): 797-808.
DOI
|
19 |
Levi, G., Hassner, T. 2015. Age and Gender Classification Using Convolutional Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34-42.
|
20 |
Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P. 2016. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 55(2): 645-657.
DOI
|
21 |
Prislan, P., Gricar, J., Cufar, K. 2014. Wood sample preparation for microscopic analysis. University of Ljubljana, Department of Wood Science and Technology.
|
22 |
Savero, A.M., Wahyudi, I., Rahayu, I.S., Yunianti, A. D., Ishiguri, F. 2020. Investigating the anatomical and physical-mechanical properties of the 8-year-old superior teakwood planted in muna island, Indonesia. Journal of the Korean Wood Science and Technology 48(5): 618-630.
DOI
|
23 |
Schoch, W., Heller, I., Schweingruber, F.H., Kienast, F. 2004. Wood Anatomy of Central European Species. Swiss Federal Institute for Forest.
|
24 |
Sewak, M., Karim, M.R., Pujari, P. 2018. Practical Convolutional Neural Networks: Implement Advanced Deep Learning Models Using Python. Packt Publishing Ltd.
|
25 |
Kwon, O., Lee, H.G., Yang, S.Y., Kim, H., Park, S.Y., Choi, I.G., Yeo, H. 2019. Performance enhancement of automatic wood classification of Korean softwood by ensembles of convolutional neural networks. Journal of the Korean Wood Science and Technology 47(3): 265-276.
DOI
|
26 |
Salma, S., Gunawan, P., Prakasa, E., Sugiarto, B., Wardoyo, R., Rianto, Y., Dewi, L.M. 2018. Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern. In: 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 23-27.
|
27 |
Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv: 1409.1556.
|