최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기디지털융복합연구 = Journal of digital convergence, v.19 no.6, 2021년, pp.251 - 258
신석용 (광운대학교 플라즈마바이오디스플레이학과) , 이상훈 (광운대학교 인제니움학부) , 한현호 (울산대학교 교양대학)
In this paper, we proposed an encoder-decoder model utilizing residual learning to improve the accuracy of the U-Net-based semantic segmentation method. U-Net is a deep learning-based semantic segmentation method and is mainly used in applications such as autonomous vehicles and medical image analys...
Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. https://doi.org/10.1109/TPAMI.2016.2572683
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, Issue Cvd, pp. 234-241). https://doi.org/10.1007/978-3-319-24574-4_28
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615
Sovetkin, E., Achterberg, E. J., Weber, T., & Pieters, B. E. (2021). Encoder-Decoder Semantic Segmentation Models for Electroluminescence Images of Thin-Film Photovoltaic Modules. IEEE Journal of Photovoltaics, 11(2), 444-452. https://doi.org/10.1109/JPHOTOV.2020.3041240
Estrada, S., Conjeti, S., Ahmad, M., Navab, N., & Reuter, M. (2018). Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks (pp. 214-222). https://doi.org/10.1007/978-3-030-00919-9_25
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016-Decem, 3213-3223. https://doi.org/10.1109/CVPR.2016.350
Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. 1-10. http://arxiv.org/abs/1606.02147
Treml, M., Arjona-medina, J., Unterthiner, T., Durgesh, R., Friedmann, F., Schuberth, P., Mayr, A., Heusel, M., Hofmarcher, M., Widrich, M., Nessler, B., & Hochreiter, S. (2016). Speeding up Semantic Segmentation for Autonomous Driving. NIPS 2016 Workshop MLITS, Nips, 1-7. https://openreview.net/pdf?idS1uHiFyyg%0Ahttps://openreview.net/forum?idS1uHiFyyg
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834-848. https://doi.org/10.1109/TPAMI.2017.2699184
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
출판사/학술단체 등이 한시적으로 특별한 프로모션 또는 일정기간 경과 후 접근을 허용하여, 출판사/학술단체 등의 사이트에서 이용 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.