參考文獻 |
[1] J. Long, E. Shelhamer, and T. Darrell. Fully Convolutional Networks for Semantic Segmentation. CVPR, 2015.
[2] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015
[3] Christian Zimmermann, Thomas Brox. Learning to Estimate 3D Hand Pose from Single RGB Images. ICCV, 2017.
[4] A. Urooj and A. Borji. Analysis of hand segmentation in the wild. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4710–4719, 2018.
[5] S. Bambach, S. Lee, D. Crandall, and C. Yu. Lending a hand: Detecting hands and recognizing activities in complex egocentric interactions. ICCV, 2015.
[6] J. Rehg, Y. Li, and Z. Ye. Delving into egocentric actions. 2015.
[7] K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. CVPR, 2016.
[8] H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia. Pyramid Scene Parsing Network. CVPR, 2017.
[9] G. Lin, A. Milan, C. Shen, and I. Reid. Refinenet: Multipath refinement networks for high-resolution semantic segmentation. CVPR, 2017.
[10] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. TPAMI 2017.
[11] Kaiming He Georgia Gkioxari Piotr Dollar Ross Girshick. Mask R-CNN. ICCV 2017.
[12] Alexander Kirillov, Yuxin Wu, Kaiming He and Ross Girshick. PointRend: Image Segmentation as Rendering. CVPR 2020.
[13] Z. Tu and X. Bai. Auto-Context and Its Applications to HighLevel Vision Tasks and 3D Brain Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009.
[14] J. Shotton, M. Johnson, and R. Cipolla. Semantic texton forests for image categorization and segmentation. CVPR, 2008.
[15] R. P. Poudel, P. Lamata, and G. Montana. Recurrent fully convolutional neural networks for multi-slice mri cardiac segmentation. In Reconstruction, Segmentation, and Analysis of Medical Images, pages 83–94. Springer, 2016.
[16] Wei Wang, Kaicheng Yu, Joachim Hugonot, Pascal Fua and Mathieu Salzmann. Recurrent U-Net for Resource-Constrained Segmentation. ICCV 2019.
[17] Sakher Ghanem, Ashiq Imran, Vassilis Athitsos. Analysis of Hand Segmentation on Challenging Hand Over Face Scenario. ACM 2019.
[18] Matti Matilainen, Pekka Sangi, Jukka Holappa, Olli Silvén. OUHANDS database for hand detection and pose recognition. IPTA 2016.
[19] Yichen Qian, Weihong Deng, Jiani Hu. Unsupervised Face Normalization with Extreme Pose and Expression in the Wild. CVPR 2019.
[20] J. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13:600–612, 2004.
[21] Sergey Ioffe, Christian Szegedy. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. ICML′15: Proceedings of the 32nd International Conference on International Conference on Machine Learning - Volume 37July 2015 Pages 448–456.
[22] Yuxin Wu, Kaiming He. Group Normalization. ECCV 2018.
[23] Hang Zhang, Han Zhang, Chenguang Wang and Junyuan Xie. Co-occurrent Features in Semantic Segmentation. CVPR 2019.
[24] Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Leichter, I., Vinnikov, A., Wei, Y., et al.: Accurate, robust, and flexible realtime hand tracking. In: ACM Conference on Human Factors in Computing Systems (2015)Diederik P. Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. ICLR, 2015.
[25] Tang, D., Taylor, J., Kohli, P., Keskin, C., Kim, T.K., Shotton, J.: Opening the black box: Hierarchical sampling optimization for estimating human hand pose. ICCV. 2015.
[26] Tompson, J., Stein, M., Lecun, Y., Perlin, K.: Real-time continuous pose recovery of human hands using convolutional networks. ACM TOG (2014)
[27] Ge, L., Liang, H., Yuan, J., Thalmann, D.: Robust 3D hand pose estimation in single depth images: from single-view cnn to multi-view cnns. CVPR 2016.
[28] Moon, G., Ju, Y.C., Lee, K.M.: V2V-PoseNet: Voxel-to-voxel prediction network for accurate 3D hand and human pose estimation from a single depth map. CVPR ,2018.
[29] Zimmermann, C., Brox, T.: Learning to estimate 3D hand pose from single RGB images. ICCV ,2017.
[30] Zhang, J., Jiao, J., Chen, M., Qu, L., Xu, X., Yang, Q.: 3D hand pose tracking and estimation using stereo matching. arXiv preprint arXiv:1610.07214 ,2016.
[31] Gyeongsik Moon , Shoou-I Yu , He Wen , Takaaki Shiratori , and Kyoung Mu Lee. InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image. ECCV 2021.
[32] Chen, L., Lin, S.Y., Xie, Y., Tang, H., Xue, Y., Xie, X., Lin, Y.Y., Fan, W.: Generating realistic training images based on tonality-alignment generative adversarial networks for hand pose estimation. arXiv preprint arXiv:1811.09916 ,2018.
[33] Yang, L., Yao, A.: Disentangling latent hands for image synthesis and pose estimation. CVPR ,2019.
[34] Spurr, A., Song, J., Park, S., Hilliges, O.: Cross-modal deep variational hand pose estimation. CVPR, 2018. |