caa a22 4500 60544692X CHVBK 20210128100129.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s11042-014-2130-z doi (NATIONALLICENCE)springer-10.1007/s11042-014-2130-z A depth estimating method from a single image using FoE CRF [Elektronische Daten] [Xiaoyan Wang, Chunping Hou, Liangzhou Pu, Yonghong Hou] A high-order conditional random field (CRF) for depth estimation from a single image is proposed in this paper. Instead of formulating the problem with the Guassian or Laplacian CRF modeling techniques, which cannot exploit the full potential offered by the probabilistic modeling, this paper proposes a depth estimation CRF model with field of experts (FoE) as the prior. The minimum mean square error (MMSE) criteria is used to infer depth. Moreover, it is assumed that the variance of depth estimation error varies spatially in depth estimation model. This allows the proposed method to enjoy the benefits offered by the flexible prior and have the advantages of making use of the non-stationary variance probability model. Experimental results indicate that the proposed method outperforms state-of-the-art approaches in terms of RMSE-error and log10-error. Springer Science+Business Media New York, 2014 Field of experts (FoE) nationallicence Conditional random field (CRF) nationallicence Non-stationary spatially variance nationallicence Minimum mean square error (MMSE) nationallicence Wang Xiaoyan School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut Hou Chunping School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut Pu Liangzhou School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut Hou Yonghong School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/21(2015-11-01), 9491-9506 1380-7501 74:21<9491 2015 74 11042 https://doi.org/10.1007/s11042-014-2130-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-2130-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Xiaoyan School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut NATIONALLICENCE 700 1- Hou Chunping School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut NATIONALLICENCE 700 1- Pu Liangzhou School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut NATIONALLICENCE 700 1- Hou Yonghong School of Electronic Information Engineering, Tianjin University, 300072, Tianjin, People's Republic of China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/21(2015-11-01), 9491-9506 1380-7501 74:21<9491 2015 74 11042