Análise de Técnicas de Deep Learning para a Medição de Fibras Microscópicas
Resumo
O objetivo principal deste estudo é revisar e explorar técnicas avançadas de segmentação de imagens a nível de instância, com base em aprendizado profundo. O propósito final é aplicar essas técnicas para a medição automática de fibras poliméricas microscópicas fornecidas pelo Prof. Dr. Douglas Cardoso Dragunski. O estudo baseia-se na revisão abrangente da literatura existente, abordando algoritmos de segmentação e medição de imagens, tanto em escala microscópica quanto não, para informar o desenvolvimento de uma solução inovadora.
Palavras-chave:
Medição de fibras, Deep Learning, Segmentação de Instância
Referências
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Y. Aslam and S. N, “A review of deep learning approaches for image analysis,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019, pp. 709–714.
M. Modarres, R. Aversa, S. Cozzini, R. Ciancio, A. Leto, and G. Brandino, “Neural network for nanoscience scanning electron microscope image recognition,” Scientific Reports, vol. 7, 10 2017.
S. Zhang, X. Liang, X. Huang, K. Wang, and T. Qiu, “Precise and fast microdroplet size distribution measurement using deep learning,” Chemical Engineering Science, vol. 247, p. 116926, 2022. [Online]. Available: [link].
A. Triki, B. Bouaziz, J. Gaikwad, and W. Mahdi, “Deep leaf: Mask r-cnn based leaf detection and segmentation from digitized herbarium specimen images,” Pattern Recognition Letters, vol. 150, pp. 76–83, 2021. [Online]. Available: [link].
D. Yang, X. Wang, H. Zhang, Z. yu Yin, D. Su, and J. Xu, “A mask r-cnn based particle identification for quantitative shape evaluation of granular materials,” Powder Technology, vol. 392, pp. 296–305, 2021. [Online]. Available: [link].
M. Ge, F. Su, Z. Zhao, and D. Su, “Deep learning analysis on microscopic imaging in materials science,” Materials Today Nano, vol. 11, p. 100087, 2020. [Online]. Available: [link].
Z. Liu, L. Jin, J. Chen, Q. Fang, S. Ablameyko, Z. Yin, and Y. Xu, “A survey on applications of deep learning in microscopy image analysis,” Computers in Biology and Medicine, vol. 134, p. 104523, 2021. [Online]. Available: [link].
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980–2988.
K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, Massachusetts, London, England: The MIT Press, 2012. [Online]. Available: [link]
M. C. Monard and J. A. Baranauskas, “Conceitos sobre aprendizado de máquina,” in Sistemas Inteligentes Fundamentos e Aplicações, 1st ed. Barueri-SP: Manole Ltda, 2003, pp. 89–114.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse, “Everything you wanted to know about deep learning for computer vision but were afraid to ask,” in 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 2017, pp. 17–41.
F. Xing, Y. Xie, H. Su, F. Liu, and L. Yang, “Deep learning in microscopy image analysis: A survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4550–4568, 2018.
P. Bharati and A. Pramanik, “Deep learning techniques—r-cnn to mask r-cnn: A survey,” in Computational Intelligence in Pattern Recognition, A. K. Das, J. Nayak, B. Naik, S. K. Pati, and D. Pelusi, Eds. Singapore: Springer Singapore, 2020, pp. 657–668.
X. L. Hao Wu, Qi Liu, “A review on deep learning approaches to image classification and object segmentation,” Computers, Materials & Continua, vol. 60, no. 2, pp. 575–597, 2019. [Online]. Available: [link]
Y. Aslam and S. N, “A review of deep learning approaches for image analysis,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), 2019, pp. 709–714.
M. Modarres, R. Aversa, S. Cozzini, R. Ciancio, A. Leto, and G. Brandino, “Neural network for nanoscience scanning electron microscope image recognition,” Scientific Reports, vol. 7, 10 2017.
S. Zhang, X. Liang, X. Huang, K. Wang, and T. Qiu, “Precise and fast microdroplet size distribution measurement using deep learning,” Chemical Engineering Science, vol. 247, p. 116926, 2022. [Online]. Available: [link].
A. Triki, B. Bouaziz, J. Gaikwad, and W. Mahdi, “Deep leaf: Mask r-cnn based leaf detection and segmentation from digitized herbarium specimen images,” Pattern Recognition Letters, vol. 150, pp. 76–83, 2021. [Online]. Available: [link].
D. Yang, X. Wang, H. Zhang, Z. yu Yin, D. Su, and J. Xu, “A mask r-cnn based particle identification for quantitative shape evaluation of granular materials,” Powder Technology, vol. 392, pp. 296–305, 2021. [Online]. Available: [link].
M. Ge, F. Su, Z. Zhao, and D. Su, “Deep learning analysis on microscopic imaging in materials science,” Materials Today Nano, vol. 11, p. 100087, 2020. [Online]. Available: [link].
Z. Liu, L. Jin, J. Chen, Q. Fang, S. Ablameyko, Z. Yin, and Y. Xu, “A survey on applications of deep learning in microscopy image analysis,” Computers in Biology and Medicine, vol. 134, p. 104523, 2021. [Online]. Available: [link].
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2980–2988.
K. P. Murphy, Machine Learning: A Probabilistic Perspective. Cambridge, Massachusetts, London, England: The MIT Press, 2012. [Online]. Available: [link]
M. C. Monard and J. A. Baranauskas, “Conceitos sobre aprendizado de máquina,” in Sistemas Inteligentes Fundamentos e Aplicações, 1st ed. Barueri-SP: Manole Ltda, 2003, pp. 89–114.
M. A. Ponti, L. S. F. Ribeiro, T. S. Nazare, T. Bui, and J. Collomosse, “Everything you wanted to know about deep learning for computer vision but were afraid to ask,” in 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), 2017, pp. 17–41.
F. Xing, Y. Xie, H. Su, F. Liu, and L. Yang, “Deep learning in microscopy image analysis: A survey,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 10, pp. 4550–4568, 2018.
P. Bharati and A. Pramanik, “Deep learning techniques—r-cnn to mask r-cnn: A survey,” in Computational Intelligence in Pattern Recognition, A. K. Das, J. Nayak, B. Naik, S. K. Pati, and D. Pelusi, Eds. Singapore: Springer Singapore, 2020, pp. 657–668.
X. L. Hao Wu, Qi Liu, “A review on deep learning approaches to image classification and object segmentation,” Computers, Materials & Continua, vol. 60, no. 2, pp. 575–597, 2019. [Online]. Available: [link]
Publicado
18/10/2023
Como Citar
ORTLIEB, Nicolli Campos; MAURICIO, Claudio Roberto Marquetto; PERES, Fabiana Frata Furlan.
Análise de Técnicas de Deep Learning para a Medição de Fibras Microscópicas. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 20. , 2023, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 202-205.
DOI: https://doi.org/10.5753/latinoware.2023.236553.
