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NTIS 바로가기한국산림과학회지 = Journal of korean society of forest science, v.110 no.2, 2021년, pp.155 - 164
김태경 (서울대학교 농림생물자원학부) , 백규헌 (서울대학교 산림과학부) , 김현석 (서울대학교 농림생물자원학부)
Many studies have been conducted on developing automatic plant identification algorithms using machine learning to various plant features, such as leaves and flowers. Unlike other plant characteristics, barks show only little change regardless of the season and are maintained for a long period. Neve...
Blaanco, L.J., Travieso, C. M., Quinteiro, J. M., Hernandez, P. V., Dutta, M. K. and Singh, A. 2016. A bark recognition algorithm for plant classification using a least square support vector machine. Ninth International conference on contemporary computing 2016: 1-5.
Boudra, S., Yahiaoui, I. and Behloul, A. 2015. A comparison of multi-scale local binary pattern variants for bark image retrieval. Computer Science 9386: 764-775.
Bressane, A., Roveda, J.A.F. and Martins, A.C.G. 2015. Statistical analysis of texture in trunk images for biometric identification of tree species. Environmental Monitoring and Assessment 187(4): 212.
Brodrick, P.G., Davies, A.B. and Asner, G.P. 2019. Uncovering ecological patterns with convolutional neural networks. Trends in Ecology & Evolution 34(8): 734-745.
Carpentier, M., Giguere, P. and Gaudreault, J. 2018. Tree species identification from bark images using convolutional neural networks. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): 1075-1081.
Chi, Z., Houqiang, L. and Chao, W. 2003. Plant species recognition based on bark patterns using novel Gabor filter banks. Neural Networks and Signal Processing 2: 1035-1038.
Choi, J.E. 2019. A Tree classification model using CNN Inception v3. (Dissertation). Seoul. Ewha Womans' University.
Cimpoi, M., Maji, S. and Vedaldi, A. 2015. Deep filter banks for texture recognition and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3828-3836.
Fiel, S. and Sablatnig, R. 2011. Automated identification of tree species from images of the bark, leaves and needles. Proceedings of the 16th Computer Vision winter workshop. pp. 67-74.
Hayat, K. 2018. Multimedia super-resolution via deep learning: A survey. Digital Signal Processing 81: 198-217.
Huang, Z.-k., Huang, D.-S., Du, J.-X., Quan, Z.-h. and Gua, S.-B. 2006. Bark classification based on contourlet filter features. Intelligent Computing. pp. 1121-1126.
Kim, M.K. 2019. Bark identification using a deep learning model. Journal of Korea Multimedia Society 22(10): 1133-1141.
Krizhevsky, A., Sutskever, I. and Hinton, G.E. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25: 1097-1105.
Mata-Montero, E., Carranza-Rojas, J. 2016. Automated plant species identification: challenges and opportunities. IFIP World Information Technology Forum. pp. 26-36.
Min, A. 2020. A Study on Transfer Learning for Image Classification of Convolutional Neural Network: Based on VGG16 Deep Convolutional Neural Network. (Dissertation). Gunpo-si. Hansei University.
Mizoguchi, T., Ishii, A., Nakamura, H., Inoue, T. and Takamatsu, H. 2017. Lidar-based individual tree species classification using convolutional neural network. Proceedings of the Society of Photo-optical Instrumentation Engineers 10332: 1-7.
Park, K.H. 2013. Characteristics on Tree Shapes and Bark Types of Landscape Trees Species in Korea. (Dissertation). Gyeongsan-si. Yeungnam University.
Ratajczak, R., Bertrand, S., Crispim, C.J. and Tougne, L. 2019. Efficient bark recognition in the wild. Proceedlings of the International Conference on Computer Vision Theory and Application. pp. 240-248.
Simonyan, K. and Zisserman, A. 2015. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations 2015: 2-8.
Svab, M. 2014. Computer-vision-based tree trunk recognition. (Dissertation). Republic of Slovenia. Faculty of Computer and Information Science, University of Ljubljana.
Yoon, Y.C., Sang, J.H. and Park, S.M. 2018. Trends of plant image processing technology. 2018 Electronics and Telecommunications Trends 33(4): 54-60.
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