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Visual Saliency Prediction using Deep learning Techniques. . 2015.
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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)
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Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images. In European Conference on Computer Vision (ECCV). Zurich; 2014.
(1.37 MB)
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Hierarchical Video Representation with Trajectory Binary Partition Tree. In Computer Vision and Pattern Recognition (CVPR). Portland, Oregon; 2013.
(4.69 MB)
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Monocular Depth Ordering Using Occlusion Cues. Barcelona: Technical University of Catalonia; 2011 .
. Monocular Depth Estimation in Images and Sequences using Occlusion Cues. . Signal Theory and Communications. 2014. p. 250.
(107.61 MB)
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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)
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Depth Ordering on Image Sequences Using Motion Occlusions. In IEEE Int. Conf. in Image Processing, ICIP 2012. Orlando, Florida, USA; 2012.
(5.42 MB)
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Depth order estimation for video frames using motion occlusions. IET Computer Vision. 2014;8(2):152-160.
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Monocular Depth Ordering Using T-junctions and Convexity Occlusion Cues. IEEE Transactions on Image Processing. 2013;22(5): 1926 - 1939 .
(2.64 MB)
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From local occlusion cues to global depth estimation. In IEEE Int. Conf. on Acoustics Speech and Signal Processing, ICASSP 2012. Kyoto, Japan; 2012.
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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)
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SurvLIMEpy: A Python package implementing SurvLIME. Expert Systems With Applications. 2024;237, Part C.
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