Search Publication
Export 557 results:
Author [ Title
] Type Year Filters: First Letter Of Last Name is C [Clear All Filters]
. Co-filtering human interaction and object segmentation. . 2015. 
(1.82 MB)
(1.82 MB). Collaborative voting of 3D features for robust gesture estimation. In International Conference on Acoustics, Speech and Signal Processing. New Orleans, USA; 2017. (0 bytes)
(0 bytes). A compact 3D representation for multi-view video. In 2011 International Conference on 3D Imaging. 2011. pp. 1–8. (4.14 MB)
(4.14 MB). Comparing Fixed and Adaptive Computation Time for Recurrent Neural Network. In International Conference on Learning Representations (ICLR). Vancouver, Canada; 2018. (515.54 KB)
(515.54 KB). 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)
(142.72 KB). Computers in the Human Interaction Loop. In Handbook on Ambient Intelligence and Smart Environments (AISE). Boston, MA: Springer; 2010. pp. 1071–1116.
. Computers in the Human Interaction Loop. In Handbook on Ambient Intelligence and Smart Environments (AISE). Boston, MA: Springer; 2010. pp. 1071–1116.
. 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.
. 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)
(878.64 KB). 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.
. Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks. Image and Vision Computing. 2019;83-84. (3.95 MB)
(3.95 MB). 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)
(521.82 KB). Crowdsourced Object Segmentation with a Game. In ACM Workshop on Crowdsourcing for Multimedia (CrowdMM). Barcelona; 2013. (1.22 MB)
(1.22 MB). Crowdsourced Object Segmentation with a Game. . 2013. (1.34 MB)
(1.34 MB). Crowdsourced Object Segmentation with a Game. In ACM Workshop on Crowdsourcing for Multimedia (CrowdMM). Barcelona; 2013. (1.22 MB)
(1.22 MB). Crowdsourced Object Segmentation with a Game. . 2013. (1.34 MB)
(1.34 MB). Cultural Event Recognition with Visual ConvNets and Temporal Models. In CVPR ChaLearn Looking at People Workshop 2015. 2015. (1.09 MB)
(1.09 MB). 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.
. 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.
. 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.
. Deep Learning that Scales: Leveraging Compute and Data. . Computer Architecture. [Barcelona, Catalonia]: Universitat Politècnica de Catalunya; 2020. (8.55 MB)
(8.55 MB). 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)
(144.69 KB). Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network. Sensors. 2019;19(8). (4.75 MB)
(4.75 MB). Description Schemes for Video Programs, Users and Devices. Signal processing: image communication. 2000;16:211–234. (879.4 KB)
(879.4 KB). Detect Snap Points in Egocentric Images with Physiological Signals. . 2016. (4.63 MB)
(4.63 MB)