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] Type Year Filters: First Letter Of Last Name is R [Clear All Filters]
. Multi-modal Deep Learning for Fuji Apple Detection Using RGB-D Cameras and their Radiometric Capabilities. Computers and Electronics in Agriculture. 2019;162.
. Multimodal imaging System based on solid-State LiDAR for Advanced perception applications. In 10th International Symposium on Optronics in defence & security. Versailles, France: 3AF OPTRO2022; 2022.
. Multimodal imaging System based on solid-State LiDAR for Advanced perception applications. In 10th International Symposium on Optronics in defence & security. Versailles, France: 3AF OPTRO2022; 2022.
. Multimodal imaging System based on solid-State LiDAR for Advanced perception applications. In 10th International Symposium on Optronics in defence & security. Versailles, France: 3AF OPTRO2022; 2022.
. Multimodal Integration of Sensor Network. In Artificial Intelligence Applications and Innovations. Boston: Springer; 2006. pp. 312–323.
. Multimodal Integration of Sensor Network. In Proceedings of 3rd IFIP Conference on Artificial Intelligence Applications & Innovations. Athens, Greece: Springer; 2006. 
(401.58 KB)
(401.58 KB). Multimodal sensing prototype for robust autonomous driving under adverse weather conditions. In SPIE Optical Metrology: Optical Measurement Systems for Industrial Inspection XIV. Munich, Germany: SPIE; 2025.
. Multimodal solid-state LiDAR for advanced perception applications. In OPTOEL. 2021. (786.62 KB)
(786.62 KB). Multimodal solid-state LiDAR for advanced perception applications. In OPTOEL. 2021. (786.62 KB)
(786.62 KB). Multimodal solid-state LiDAR for advanced perception applications. In OPTOEL. 2021. (786.62 KB)
(786.62 KB). Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation. In CVPR 2021 Robust Video Scene Understanding: Tracking and Video Segmentation (RVSU) Workshop. 2021.
. Multi-resolution illumination compensation for foreground extraction. In 16th International Conference on Image Processing. 2009. pp. 3225–3228. (2.16 MB)
(2.16 MB). Multi-View 3D Face Reconstruction in the Wild using Siamese Networks. In ICCV 2019 Workshop on 3D Face Alignment in the Wild Challenge Workshop (3DFAW). Seoul, South Corea: IEEE/CVF; 2019. (227.12 KB)
(227.12 KB). Multi-view depth coding based on a region representation combining color and depth information. . Signal Theory and Communications (TSC). Universitat Politècnica de Catalunya (UPC); 2017. (15.24 MB)
(15.24 MB). Multiview depth coding based on combined color/depth segmentation. Journal of visual communication and image representation. 2012;23(1):42–52. (1.81 MB)
(1.81 MB). New interaction modes for rich panoramic live video experiences. Behaviour & Information Technology. 2014;33(8). (278.16 KB)
(278.16 KB). Oriented radial distribution on depth data: Application to the detection of end-effectors. In IEEE International Conference on Acoustics, Speech, and Signal Processing. Kyoto, Japan; 2012. (1.21 MB)
(1.21 MB). Perception in the era of Autonomous Vehicles. In Photonics 4 Smart Cities, SCEWC 2022. Barcelona: Photonics21; 2022. (2.34 MB)
(2.34 MB). Performance of Individual Tree Segmentation Algorithms in Forest Ecosystems Using UAV LiDAR Data. Drones. 2024;8(12).
. Plug-and-Train Loss for Model-Based Single View 3D Reconstruction. BMVA Technical Meeting: 3D vision with Deep Learning. London, UK: UPC; 2019. (3.97 MB)
(3.97 MB). Plug-and-Train Loss for Model-Based Single View 3D Reconstruction. BMVA Technical Meeting: 3D vision with Deep Learning. London, UK: UPC; 2019. (3.97 MB)
(3.97 MB). Predicting Dog Phenotypes from Genotypes. In 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'22). 2022. (1.17 MB)
(1.17 MB). Prediction of a second clinical event in CIS patients by combining lesion and brain features. In Congress of the European Comitee for Treatment and Research in Multiple Sclerosis (ECTRIMS 2019). 2019.
. Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI. In Alzheimer's Association International Conference. 2021.
. A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection. Sensors. 2023;23(8). (9.94 MB)
(9.94 MB)