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Palou G, Salembier P. 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)
Palou G, Salembier P. 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)
Palou G, Salembier P. Hierarchical Video Representation with Trajectory Binary Partition Tree. In Computer Vision and Pattern Recognition (CVPR). Portland, Oregon; 2013.  (4.69 MB)
Palou G, Salembier P. Depth order estimation for video frames using motion occlusions. IET Computer Vision. 2014;8(2):152-160.  (910.25 KB)
Palou G. Monocular Depth Estimation in Images and Sequences using Occlusion Cues. Salembier P. Signal Theory and Communications. 2014. p. 250.  (107.61 MB)
Palou G, Salembier P. 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)
Palou G, Salembier P. Monocular Depth Ordering Using Occlusion Cues. Barcelona: Technical University of Catalonia; 2011 .
Pan J, Giró-i-Nieto X. 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)
Pan J, Canton-Ferrer C, McGuinness K, O'Connor N, Torres J, Sayrol E, et al.. SalGAN: Visual Saliency Prediction with Generative Adversarial Networks. In CVPR 2017 Scene Understanding Workshop (SUNw). Honolulu, Hawaii, USA; 2017.  (1.85 MB)
Pan J. Visual Saliency Prediction using Deep learning Techniques. Giró-i-Nieto X. 2015.  (1.57 MB)
Pan J, McGuinness K, Sayrol E, O'Connor N, Giró-i-Nieto X. 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)
Panizo E. Classification techniques for Alzheimer’s disease early diagnosis. Vilaplana V. 2015.  (5.86 MB)
Pardàs M. Object-base image coding. Vistas in astronomy. 1997;41:455–461.
Pardàs M, Bonafonte A. Work in progress - Cooperative and competitive projects for engaging students in advanced ICT subjects. In 41st Annual Frontiers in Education Conference. 2011. pp. 1–3.
Pardàs M, Sayrol E. A new approach to active contours for tracking. In IEEE International Conference on Image Processing. 2000.
Pardàs M, Salembier P. Joint region and motion estimation with morphological tools. In International Symposium on Mathematical Morphology, ISMM 1994. Fontainebleau, France; 1994.
Pardàs M, Sayrol E. Motion estimation based tracking of active contours. Pattern recognition letters. 2001;22:1447–1456.
Pardàs M, Salembier P. Time-recursive segmentation of image sequences. In European Signal Processing Conference, EUSIPCO-94. Edinburgh, UK; 1994. pp. 18–21.  (806.06 KB)
Pardàs M. Relative depth estimation and segmentation in monocular sequences. In 1997 PICTURE CODING SYMPOSIUM. 1997. pp. 367–372.
Pardàs M, Anglada D, Espina M, Marques F, Salembier P. Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation. JOURNAL OF MEDICAL IMAGING. 2023;10(3).  (13.78 MB)
Pardàs M, Salembier P, Torres L. 3D morphological segmentation or image sequence processing. In IEEE Winter Workshop on Nonlinear Signal Processing. Tampere, Finland; 1993. pp. 31–36.
Pardàs M, Salembier P. 3d morphological segmentation and motion estimation for image sequences. Signal processing. 1994;38:31–43.
Pardàs M, Marcos L. Facial Parameter Extraction System based on Active Contours. In IEEE International Conference on Image Processing. 2001. pp. 1058–1061.
Pardàs M. Video Object Segmentation introducing depth and motion information. In IEEE International Conference on Image Processing. 1998.
Pardàs M, Canet G. Refinement network for unsupervised on the scene foreground segmentation. In EUSIPCO European Signal Processing Conference. European Association for Signal Processing (EURASIP); 2020.

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