[1] Naeemabadi M Reza, Dinesen Birthe, Andersen Ole Kaeseler, et al. Influence of a Marker-based motion capture system on the performance of microsoft Kinect v2 skeleton algorithm[J]. IEEE Sensors Journal, 2019, 19(1): 171-179.

[2] Zhao Min, Xiong Zhaolong, Xing Yan, et al. Real-time integral imaging pickup system based on binocular stereo camera[J]. Infrared and Laser Engineering, 2017, 46(11): 1103007. (in Chinese)

[3] Guo Chunle, Li Chongyi, Guo Jichang, et al. Hierarchical features driven residual learning for depth map super-resolution[J]. IEEE Transaction on Image Processing, 2019, 28(5): 2545-2557.

[4] Chen Guangda, Cui Guowei, Jin Zhongxiao, et al. Accurate intrinsic and extrinsic calibration of RGB-D cameras w with GP-based depth correction [J]. IEEE Sensors Journal, 2019, 19(7): 2685-2694.

[5] Cao Ting, Wang Weixing. Detection method for the depth of pavement broken block in cement concrete based on 3D laser scanning technology[J], Infrared and Laser Engineering, 2017, 46(2): 0206006. (in Chinese)

[6] Moon Sungphill, Park Youngbin, Ko Dong Wook, et al. Multiple kinect sensor fusion for human skeleton tracking using Kalman filtering[J]. International Journal of Advanced Robotic Systems, 2016, 13(65): 1-10.

[7] Boutellaa Elhocine, Hadid Abdenour, Bengherabi Messaoud, et al. On the use of Kinect depth data for identity, gender and ethnicity classification from facial images [J]. Pattern Recognition Letters, 2015, 68(2): 270-277.

[8] Hu Jinhui, Hu Ruimin, Wang Zhongyuan, et al. Color image guided locality regularized representation for depth holes filling[C]//IEEE International Conference on Visual Communications and Image Processing (VCIP), 2013: 17-20.

[9] Yang Jingyu, Gan Ziqiao, Li Kun, et al. Graph-based segmentation for RGB-D data using 3-D geometry enhanced superpixels [J]. IEEE Trans Cybern, 2015, 45(5): 913-926.

[10] Camplani Massimo, Salgado Luis. Efficient spatio-temporal hole filling strategy for Kinect depth maps[C]//Three-Dimensional Image Processing (3DIP) and Applications, 2012: 8290.

[11] Maimone Andrew, Bidwell Jonathan, Peng Kun, et al. Enhanced personal auto stereoscopic telepresence system using commodity depth cameras [J]. Comput Graph, 2012, 36(7): 791-807.

[12] Essmaeel Kyis, Gallo Luigi, Damiani Ernesto, et al. Comparative evaluation of methods for filtering Kinect depth data[J]. Multimed Tools Appl, 2015, 74(17): 7331-7354.

[13] Deng Teng, Li Hui, Cai Jianfei, et al. Kinect shadow detection and classification[C]//IEEE International Conference on Computer Vision Workshops, 2013: 708-713.

[14] Ma Ronggui, Cao Ting, Wang Weixing. HUD image vibration detection on improved edge detection and corner extraction [J] International Journal of Signal Processing, 2014, 7(1): 393-404.

[15] Pu Yifei, Zhou Jiliu, Yuan Xiao. Fractional differential mask: a fractional differential-based approach for milti scale texture enhancement [J]. IEEE Transactions on Image Processing, 2010, 19(2): 491-511.

[16] Perrin Emmanuel, Harba Rachid, Corinne Berzin-Joseph, et al. nth-order fractional Brownian motion and fractional Gaussian noises [J]. IEEE Transactions on Signal Processing, 2001, 49(5): 1049-1059.

[17] Hu Fuyuan, Si Shaohui, San Wong, et al. An adaptive approach for texture enhancement based on a fractional differential operator with non-integer step and order [J]. Neurocomputing, 2015, 158: 295-306.

[18] Cao Ting, Wang Weixing, Tighe Susan, et al. Crack image detection based on fractional differential and fractal dimension [J]. IET Computer Vision, 2019, 13(1): 79-58.

[19] Li Bo, Xie Lei. Image denoising and enhancement based on adaptive fractional calculus of small probability strategy [J]. Neurocomputing, 2016, 175: 704-717.

[20] Cao Ting, Yang Nan, Wang Fengping, et al. Crack image segmentation based on improved DBC method [C]//SPIE Conference on LIDAR Imaging Detection and Target Recognition, 2017, 15(11): 106052B.