Deep Learning-Driven Depth from Defocus via Active Multispectral Quasi-Random Projections with Complex Subpatterns
暂无分享,去创建一个
[1] Liu Jianzhuang,et al. Automatic thresholding of gray-level pictures using two-dimension Otsu method , 1991, China., 1991 International Conference on Circuits and Systems.
[2] Robert Bridson,et al. Fast Poisson disk sampling in arbitrary dimensions , 2007, SIGGRAPH '07.
[3] Stefano Soatto,et al. Observing Shape from Defocused Images , 1999, Proceedings 10th International Conference on Image Analysis and Processing.
[4] A. Ma,et al. Depth from Defocus via Active Quasi-random Point Projections , 2016 .
[5] David A. Clausi,et al. Depth from Defocus via Active Quasi-random Point Projections: A Deep Learning Approach , 2017, ICIAR.
[6] Paul F. Whelan,et al. Computational approach for depth from defocus , 2005, J. Electronic Imaging.
[7] Richard Szeliski,et al. High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..
[8] A. Ma,et al. Enhanced depth from defocus via active quasi-random colored point projections , 2017 .
[9] Francesc Moreno-Noguer,et al. Active refocusing of images and videos , 2007, ACM Trans. Graph..
[10] Alex Pentland,et al. Simple range cameras based on focal error , 1994 .
[11] H. Niederreiter. Point sets and sequences with small discrepancy , 1987 .
[12] Joaquim Salvi,et al. Pattern codification strategies in structured light systems , 2004, Pattern Recognit..
[13] David A. Clausi,et al. Depth from Defocus via Active Multispectral Quasi-random Point Projections using Deep Learning , 2017 .