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Cabezas F. Co-filtering human interaction and object segmentation. Carlier A, Salvador A, Giró-i-Nieto X, Charvillat V. 2015.  (1.82 MB)
Salavedra-Pujol J, Wang M, Ruiz-Hidalgo J, Casas JR. Collaborative Perception for Autonomous Driving: From Virtual To Real Datasets. In IEEE International Conference on Image Processing (ICIP), 2nd workshop on Learning Beyond Deep Learning (LBDL II). Tampere: IEEE; 2026.
van Sabben D, Ruiz-Hidalgo J, Suau X, Casas JR. Collaborative voting of 3D features for robust gesture estimation. In International Conference on Acoustics, Speech and Signal Processing. New Orleans, USA; 2017.  (0 bytes)
Salvador J, Casas JR. A compact 3D representation for multi-view video. In 2011 International Conference on 3D Imaging. 2011. pp. 1–8.  (4.14 MB)
Fojo D, Campos V, Giró-i-Nieto X. Comparing Fixed and Adaptive Computation Time for Recurrent Neural Network. In International Conference on Learning Representations (ICLR). Vancouver, Canada; 2018.  (515.54 KB)
Valero S, Salembier P, Chanussot J. Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data. In IEEE International Conference on Image Processing, ICIP 2010. Hong Kong, China; 2010. pp. 2565–2568.  (142.72 KB)
Waibel A, Stiefelhagen R, Carlson R, Casas JR, Kleindienst J, Lamel L, et al.. Computers in the Human Interaction Loop. In Handbook on Ambient Intelligence and Smart Environments (AISE). Boston, MA: Springer; 2010. pp. 1071–1116.
Waibel A, Stiefelhagen R, Carlson R, Casas JR, Kleindienst J, Lamel L, et al.. Computers in the Human Interaction Loop. In Handbook on Ambient Intelligence and Smart Environments (AISE). Boston, MA: Springer; 2010. pp. 1071–1116.
Alcoverro M, López-Méndez A, Pardàs M, Casas JR. Connected Operators on 3D data for human body analysis. In 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2011. pp. 9–14.
Casas JR, Neumann J. Context Awareness triggered by Multiple Perceptual Analyzers. In Emerging Artificial Intelligence Applications in Computer Engineering. Amsterdam: IOS Press; 2007. pp. 371–383.  (878.64 KB)
Hernandez C, Combalia M, Puig S, Malvehy J, Vilaplana V. Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours. In Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). 2022.
Pujol-Miró A, Casas JR, Ruiz-Hidalgo J. Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks. Image and Vision Computing. 2019;83-84.  (3.95 MB)
Górriz M, Giró-i-Nieto X, Carlier A, Faure E. Cost-Effective Active Learning for Melanoma Segmentation. In ML4H: Machine Learning for Health Workshop at NIPS 2017. Long Beach, CA, USA; 2017.  (521.82 KB)
Salvador A, Carlier A, Giró-i-Nieto X, Marques O, Charvillat V. Crowdsourced Object Segmentation with a Game. In ACM Workshop on Crowdsourcing for Multimedia (CrowdMM). Barcelona; 2013.  (1.22 MB)
Salvador A. Crowdsourced Object Segmentation with a Game. Giró-i-Nieto X, Carlier A, Charvillat V, Marques O. 2013.  (1.34 MB)
Salvador A, Carlier A, Giró-i-Nieto X, Marques O, Charvillat V. Crowdsourced Object Segmentation with a Game. In ACM Workshop on Crowdsourcing for Multimedia (CrowdMM). Barcelona; 2013.  (1.22 MB)
Salvador A. Crowdsourced Object Segmentation with a Game. Giró-i-Nieto X, Carlier A, Charvillat V, Marques O. 2013.  (1.34 MB)
Salvador A, Zeppelzauer M, Manchon-Vizuete D, Calafell A, Giró-i-Nieto X. Cultural Event Recognition with Visual ConvNets and Temporal Models. In CVPR ChaLearn Looking at People Workshop 2015. 2015.  (1.09 MB)
D
Casamitjana A, Sala-Llonch R, Tudela R, Andrés A, Orío S, Casas JR, et al.. Deep Learning CT segmentation for dosimetry in postoperative endometrial carcinoma treatment. In XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica. Cartagena: Ediciones UPCT. Universidad Politécnica de Cartagena; 2023.  (236.12 KB)
Casamitjana A, Sala-Llonch R, Tudela R, Andrés A, Orío S, Casas JR, et al.. Deep Learning CT segmentation for dosimetry in postoperative endometrial carcinoma treatment. In XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica. Cartagena: Ediciones UPCT. Universidad Politécnica de Cartagena; 2023.  (236.12 KB)
Casamitjana A, Sala-Llonch R, Tudela R, Andrés A, Orío S, Casas JR, et al.. Deep Learning CT segmentation for dosimetry in postoperative endometrial carcinoma treatment. In XLI Congreso Anual de la Sociedad Española de Ingeniería Biomédica. Cartagena: Ediciones UPCT. Universidad Politécnica de Cartagena; 2023.  (236.12 KB)
Campos V. Deep Learning that Scales: Leveraging Compute and Data. Torres J, Giró-i-Nieto X. Computer Architecture. [Barcelona, Catalonia]: Universitat Politècnica de Catalunya; 2020.  (8.55 MB)
Baldrich A, Paugam R, Casas JR, Pardàs M, Àgueda A, Parsons R, et al.. Deep learning-based methodology for smoke plume segmentation of wildfire images. In 14th International Symposium on Fire Safety Science (IAFSS2023). Tsukuba, Japan: International Association for Fire Safety Science (IAFSS); 2023.  (144.69 KB)
Lin X, Sanchez-Escobedo D, Casas JR, Pardàs M. Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network. Sensors. 2019;19(8).  (4.75 MB)
Salembier P, Richard Q, O'Connor N, Correia P, Sezan I, van Beek P. Description Schemes for Video Programs, Users and Devices. Signal processing: image communication. 2000;16:211–234.  (879.4 KB)

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