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Pardàs M. Relative depth estimation and segmentation in monocular sequences. In 1997 PICTURE CODING SYMPOSIUM. 1997. pp. 367–372.
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, 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, Sayrol E. Motion estimation based tracking of active contours. Pattern recognition letters. 2001;22:1447–1456.
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, Salembier P, Marqués F, Morros JR. Partition tree for a segmentation-based video coding system. In IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1996. Atlanta (GA), USA; 1996. pp. 1982–1985.  (196.06 KB)
Pardàs M, Salembier P. 3d morphological segmentation and motion estimation for image sequences. Signal processing. 1994;38:31–43.
Pardàs M, Bonafonte A, Landabaso J-L. Emotion recognition based on MPEG-4 facial animation parameters. In IEEE International Conference on Acoustics, Speech, and Signal Processing. 2002. pp. 3624–3627.
Pardàs M. Automatic Face Analysis for Model Calibration. In International Workshop on Synthetic and natural hybrid coding and three dimensional imaging. 1999. pp. 12–15.
Pardàs M, Salembier P, Gonzalez B. Motion region overlapping for segmentation-based video coding. In International Conference on Image Processing, ICIP'94. Austin, Texas; 1994. pp. 428–431.
Pardàs M, Salembier P, Ayuso X, Martí E. Video coding method and corresponding coding and decoding systems. 1997.
Pardàs M, Salembier P. Segmentation of video sequences for partition tree generation. Annales des télecommunications. Annals of telecommunications. 1997;52:389–396.
Pardàs M, Bonafonte A. Facial Animation Parameters extraction and Expression detection using HMM. In International Conference on Augmented, Virtual Environments and 3D Imaging. 2001. pp. 120–123.
Pardàs M, Torres L. Connectivity filters for image sequences. In IMAGE ALGEBRA AND MORPHOLOGICAL IMAGE PROCESSING. SPIE. 1992. pp. 318–329.
Pardàs M. Extraction and tracking of the eyelids. In International Conference on Acoustics, Speech and Signal Processing. 2000.
Pardàs M, Salembier P. 3D morphological segmentation and motion estimation for image sequences. In International Symposium on Mathematical Morphology and its applications to image and signal processing, ISMM 1993. Barcelona, Spain; 1993. pp. 58–63.
Pardàs M. Segmentación Morfológica de Secuencias de Imágenes: Aplicación a la Codificación. Salembier P. Universitat Politècnica de Catalunya (UPC); 1995.
Pardàs M, Bonafonte A. Facial animation parameters extraction and expression recognition using Hidden Markov Models. Signal processing: image communication. 2002;:675–688.
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. Time-recursive segmentation of image sequences. In European Signal Processing Conference, EUSIPCO-94. Edinburgh, UK; 1994. pp. 18–21.  (806.06 KB)
Panizo E. Classification techniques for Alzheimer’s disease early diagnosis. Vilaplana V. 2015.  (5.86 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, 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)

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