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. SurvLIMEpy: A Python package implementing SurvLIME. Expert Systems With Applications. 2024;237, Part C.
. Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images. In European Conference on Computer Vision (ECCV). Zurich; 2014. 
(1.37 MB)
(1.37 MB). Hierarchical Video Representation with Trajectory Binary Partition Tree. In Computer Vision and Pattern Recognition (CVPR). Portland, Oregon; 2013. (4.69 MB)
(4.69 MB). 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)
(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). Depth Ordering on Image Sequences Using Motion Occlusions. In IEEE Int. Conf. in Image Processing, ICIP 2012. Orlando, Florida, USA; 2012. (5.42 MB)
(5.42 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 Ordering Using T-junctions and Convexity Occlusion Cues. IEEE Transactions on Image Processing. 2013;22(5): 1926 - 1939 . (2.64 MB)
(2.64 MB). 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). 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). 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). 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). Visual Saliency Prediction using Deep learning Techniques. . 2015. (1.57 MB)
(1.57 MB). 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). Classification techniques for Alzheimer’s disease early diagnosis. . 2015. (5.86 MB)
(5.86 MB). The InterFace Software Platform for Interactive Virtual Characters. In Mpeg-4 facial animation: the standard, implementation and applications. 2004. pp. 169–183.
. Joint region and motion estimation with morphological tools. In International Symposium on Mathematical Morphology, ISMM 1994. Fontainebleau, France; 1994.
. Object-base image coding. Vistas in astronomy. 1997;41:455–461.
. Refinement network for unsupervised on the scene foreground segmentation. In EUSIPCO European Signal Processing Conference. European Association for Signal Processing (EURASIP); 2020.
. Relative depth estimation and segmentation in monocular sequences. In 1997 PICTURE CODING SYMPOSIUM. 1997. pp. 367–372.
. Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation. JOURNAL OF MEDICAL IMAGING. 2023;10(3). (13.78 MB)
(13.78 MB). 3D morphological segmentation or image sequence processing. In IEEE Winter Workshop on Nonlinear Signal Processing. Tampere, Finland; 1993. pp. 31–36.
. Motion estimation based tracking of active contours. Pattern recognition letters. 2001;22:1447–1456.
. Facial Parameter Extraction System based on Active Contours. In IEEE International Conference on Image Processing. 2001. pp. 1058–1061.