基于双流加权Gabor卷积网络融合的室内RGB-D图像语义分割 下载: 1113次
王旭初, 刘辉煌, 牛彦敏. 基于双流加权Gabor卷积网络融合的室内RGB-D图像语义分割[J]. 光学学报, 2020, 40(19): 1910001.
Xuchu Wang, Huihuang Liu, Yanmin Niu. Indoor RGB-D Image Semantic Segmentation Based on Dual-Stream Weighted Gabor Convolutional Network Fusion[J]. Acta Optica Sinica, 2020, 40(19): 1910001.
[1] RonnebergerO, FischerP, BroxT. U-net: convolutional networks for biomedical image segmentation[M] ∥Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015: 234- 241.
[3] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for scene segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[4] Chen LC, Zhu YK, PapandreouG, et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[M] ∥Computer Vision-ECCV 2018. Cham: Springer International Publishing, 2018: 833- 851.
[5] NohH, HongS, HanB. Learning deconvolution network for semantic segmentation[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile.New York: IEEE Press, 2015: 1520- 1528.
[6] LiuW, RabinovichA, Berg A C. ParseNet: looking wider to see better[EB/OL]. ( 2015-11-19)[2020-04-26]. https:∥arxiv.org/abs/1506. 04579.
[7] 张哲晗, 方薇, 杜丽丽, 等. 基于编码-解码卷积神经网络的遥感图像语义分割[J]. 光学学报, 2020, 40(3): 0310001.
[8] YuF, Koltun V. Multi-scale context aggregation by dilated convolutions[EB/OL]. ( 2016-04-30)[2020-04-26]. https:∥arxiv.org/abs/1511. 07122.
[9] 吴止锾, 高永明, 李磊, 等. 类别非均衡遥感图像语义分割的全卷积网络方法[J]. 光学学报, 2019, 39(4): 0428004.
[10] Lin GS, MilanA, Shen CH, et al.RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 5168- 5177.
[11] 胡涛, 李卫华, 秦先祥. 基于多层深度特征融合的极化合成孔径雷达图像语义分割[J]. 中国激光, 2019, 46(2): 0210001.
[12] Wang PQ, Chen PF, YuanY, et al.Understanding convolution for semantic segmentation[C]∥2018 IEEE Winter Conference on Applications of Computer Vision (WACV), March 12-15, 2018, Lake Tahoe, NV, USA.New York: IEEE Press, 2018: 1451- 1460.
[13] ZhengS, JayasumanaS, Romera-ParedesB, et al.Conditional random fields as recurrent neural networks[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile.New York: IEEE Press, 2015: 1529- 1537.
[14] Lin GS, Shen C H, van den Hengel A, et al. Efficient piecewise training of deep structured models for semantic segmentation[C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA.New York: IEEE Press, 2016: 3194- 3203.
[15] ArnabA, JayasumanaS, ZhengS, et al.Higher order conditional random fields in deep neural networks[M] ∥Computer Vision-ECCV 2016. Cham: Springer International Publishing, 2016: 524- 540.
[16] Ren XF, Bo LF, FoxD. RGB-(D)scene labeling: features and algorithms[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA. New York: IEEE Press, 2012: 2759- 2766.
[17] SilbermanN, HoiemD, KohliP, et al. Indoor segmentation and support inference from RGBD images[M] ∥Computer Vision-ECCV 2012. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012: 746- 760.
[18] HeY, Chiu WC, KeuperM, et al.STD2P: RGBD semantic segmentation using spatio-temporal data-driven pooling[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE Press, 2017: 7158- 7167.
[19] Cheng YH, CaiR, Li ZW, et al.Locality-sensitive deconvolution networks with gated fusion for RGB-D indoor semantic segmentation[C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA.New York: IEEE Press, 2017: 1475- 1483.
[20] Yurdakul EE, YemezY. Semantic segmentation of RGBD videos with recurrent fully convolutional neural networks[C]∥2017 IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017, Venice, Italy.New York: IEEE Press, 2017: 367- 374.
[21] Hu XX, Yang KL, FeiL, et al.ACNET: attention based network to exploit complementary features for RGBD semantic segmentation[C]∥2019 IEEE International Conference on Image Processing (ICIP), September 22-25, 2019, Taipei, Taiwan, China. New York: IEEE Press, 2019: 1440- 1444.
[22] Lin D, Zhang R M, Ji Y F, et al. SCN: switchable context network for semantic segmentation of RGB-D images[J]. IEEE Transactions on Cybernetics, 2020, 50(3): 1120-1131.
[24] Luan S Z, Chen C, Zhang B C, et al. Gabor convolutional networks[J]. IEEE Transactions on Image Processing, 2018, 27(9): 4357-4366.
[25] ZagoruykoS, Komodakis N. Wide residual networks[EB/OL]. ( 2017-06-14) [2020-04-26]. https:∥arxiv.org/abs/1605. 07146.
[27] JanochA, KarayevS, Jia YQ, et al.A category-level 3-D object dataset: putting the Kinect to work[C]∥2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), November 6-13, 2011, Barcelona, Spain. New York: IEEE Press, 2011: 1168- 1174.
[28] Xiao JX, OwensA, TorralbaA. SUN3D: a database of big spaces reconstructed using SfM and object labels[C]∥2013 IEEE International Conference on Computer Vision, December 1-8 2013, Sydney, NSW, Australia. New York: IEEE Press, 2013: 1625- 1632.
王旭初, 刘辉煌, 牛彦敏. 基于双流加权Gabor卷积网络融合的室内RGB-D图像语义分割[J]. 光学学报, 2020, 40(19): 1910001. Xuchu Wang, Huihuang Liu, Yanmin Niu. Indoor RGB-D Image Semantic Segmentation Based on Dual-Stream Weighted Gabor Convolutional Network Fusion[J]. Acta Optica Sinica, 2020, 40(19): 1910001.