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(5.42 MB). Occlusion-based depth ordering on monocular images with binary partition tree. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011. Prague, Czech Republic; 2011. pp. 1093–1096. (444.04 KB)
(444.04 KB). From local occlusion cues to global depth estimation. In IEEE Int. Conf. on Acoustics Speech and Signal Processing, ICASSP 2012. Kyoto, Japan; 2012. (480.32 KB)
(480.32 KB). Hierarchical Video Representation with Trajectory Binary Partition Tree. In Computer Vision and Pattern Recognition (CVPR). Portland, Oregon; 2013. (4.69 MB)
(4.69 MB). Depth order estimation for video frames using motion occlusions. IET Computer Vision. 2014;8(2):152-160. (910.25 KB)
(910.25 KB). Monocular Depth Estimation in Images and Sequences using Occlusion Cues. . Signal Theory and Communications. 2014. p. 250. (107.61 MB)
(107.61 MB). 2.1 Depth Estimation of Frames in Image Sequences Using Motion Occlusions. In Computer Vision – ECCV 2012. Workshops and Demonstrations. Springer Berlin Heidelberg; 2012. (8.88 MB)
(8.88 MB). Monocular Depth Ordering Using Occlusion Cues. Barcelona: Technical University of Catalonia; 2011 .
. End-to-end Convolutional Network for Saliency Prediction. Large-scale Scene Understanding Challenge (LSUN) at CVPR Workshops . Boston, MA (USA): arXiv; 2015 . (1.18 MB)
(1.18 MB). SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. In CVPR 2017 Scene Understanding Workshop (SUNw). Honolulu, Hawaii, USA; 2017. (1.85 MB)
(1.85 MB). Visual Saliency Prediction using Deep learning Techniques. . 2015. (1.57 MB)
(1.57 MB). Shallow and Deep Convolutional Networks for Saliency Prediction. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR. Las Vegas, NV, USA: Computer Vision Foundation / IEEE; 2016. (466.13 KB)
(466.13 KB). Classification techniques for Alzheimer’s disease early diagnosis. . 2015. (5.86 MB)
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