基于双流卷积神经网络的RGB-D图像联合检测 下载: 1329次
刘帆, 刘鹏远, 张峻宁, 徐彬彬. 基于双流卷积神经网络的RGB-D图像联合检测[J]. 激光与光电子学进展, 2018, 55(2): 021503.
fan Liu, Pengyuan Liu, Junning Zhang, Binbin Xu. Joint Detection of RGB-D Images Based on Double Flow Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021503.
[1] 崔波. 基于RGB-D信息的显著物体检测[D]. 天津: 天津大学, 2014: 17- 26.
CuiB. Significant object detection based on RGB-D information[D]. Tianjin: Tianjin University, 2014: 17- 26.
[2] GuptaS, GirshickR, ArbeláezP, et al. Learning rich features from RGB-D images for object detection and segmentation[C]. European Conference on Computer Vision, 2014, 8695: 345- 360.
[3] CouprieC, FarabetC, NajmanL, et al. Indoor semantic segmentation using depth information [J]. arXiv: 1301. 3572v2.
[4] GuptaS, ArbelaezP, MalikJ. Perceptual organization and recognition of indoor scenes from RGB-D images[C]. IEEE Computer Vision and Pattern Recognition, 2013: 564- 571.
[5] EitelA, Springenberg JT, SpinelloL, et al. Multimodal deep learning for robust RGB-D object recognition[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, 2015: 681- 687.
[6] 芮挺, 费建超, 周遊, 等. 基于深度卷积神经网络的行人检测[J]. 计算机工程与应用, 2016, 52(13): 163-168.
Rui T, Fei J C, Zhou Y, et al. Pedestrian detection based on deep convolutional neural network[J]. Computer Engineering and Application, 2016, 52(13): 163-168.
[7] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理, 2016, 31(1): 1-17.
Lu H T, Zhang Q C. Overview of application of depth convolutional neural network in computer vision[J]. Data Acquisition and Processing, 2016, 31(1): 1-17.
[8] SongS, Xiao JX. Deep sliding shapes for amodal 3D object detection in RGB-D images[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2016: 808- 816.
[9] 涂淑琴, 薛月菊, 梁云, 等. RGB-D图像分类方法研究综述[J]. 激光与光电子学进展, 2016, 53(6): 060003.
[10] 卢良锋, 谢志军, 叶宏武. 基于RGB-D特征与深度特征融合的物体识别算法[J]. 计算机工程, 2015, 42(5): 187-192.
Lu L F, Xie Z J, Ye H W. Object recognition algorithm based on RGB-D feature and depth feature fusion[J]. Computer Engineering, 2015, 42(5): 187-192.
[11] 张俊, 李鑫. TensorFlow平台下的手写字符识别[J]. 电脑知识, 2016, 12(16): 199-201.
Zhang J, Li X. Handwritten character recognition based on TensorFlow platform[J]. Computer Knowledge, 2016, 12(16): 199-201.
[12] 张炜. 基于机器学习的智能家居系统设计与实现[D]. 吉林: 吉林大学, 2016: 25- 37.
ZhangW. Design and implementation of intelligent home system based on machinelearning[D]. Jilin: Jilin University, 2016: 25- 37.
[13] LaiK, BoL, RenX, et al. A large-scale hierarchical multi-view RGB-D object dataset[C]. IEEE International Conference on Robotics and Automation, 2011: 1817- 1824.
[14] 毛宁, 杨德东, 杨福才, 等. 基于分层卷积特征的自适应目标跟踪[J]. 激光与光电子学进展, 2016, 53(12): 121501.
[15] JiaY, ShelhamerE, DonahueJ, et al. Caffe: Convolutional architecture for fast feature embedding[C]. ACM International Conference on Multimedia, 2014: 675- 678.
[16] 蔡强, 魏立伟, 李海生, 等. 基于ANNet网络的 RGB-D 图像的目标检测[J]. 系统仿真学报, 2016, 28(9): 2260-2266.
Cai Q, Wei L W, Li H S, et al. Target detection of RGB-D images based on ANNet networks[J]. Journal of Systems Simulation, 2016, 28(9): 2260-2266.
刘帆, 刘鹏远, 张峻宁, 徐彬彬. 基于双流卷积神经网络的RGB-D图像联合检测[J]. 激光与光电子学进展, 2018, 55(2): 021503. fan Liu, Pengyuan Liu, Junning Zhang, Binbin Xu. Joint Detection of RGB-D Images Based on Double Flow Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021503.