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12 p, 2.9 MB |
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation : The MMs Challenge
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Campello, Victor M. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Gkontra, Polyxeni (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Izquierdo, Cristian (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Martin-Isla, Carlos (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Sojoudi, Alireza (Circle Cardiovascular Imaging Pvt. Ltd.) ;
Full, Peter M. (German Cancer Research Center) ;
Maier-Hein, Klaus (Division of Medical Image Computing. German Cancer Research Center) ;
Zhang, Yao (Chinese Academy of Sciences. Institute of Computing Technology) ;
He, Zhiqiang (Lenovo Ltd.) ;
Ma, Jun (Nanjing University of Science and Technology) ;
Parreno, Mario (Universitat Politècnica de València) ;
Albiol, Alberto (Universitat Politècnica de València. iTeam Research Institute) ;
Kong, Fanwei (University of California at Berkeley. Department of Mechanical Engineering) ;
Shadden, Shawn C. (University of California at Berkeley. Department of Mechanical Engineeringy) ;
Corral Acero, Jorge (Institute of Biomedical Engineering. Department of Engineering Science. University of Oxford) ;
Sundaresan, Vaanathi (University of Oxford. Nuffield Department of Clinical Neurosciences) ;
Saber, Mina (Research and Development Division. Intixel Company S.A.E.) ;
Elattar, Mustafa (Research and Development Division. Intixel Company S.A.E.) ;
Li, Hongwei (Department of Computer Science. Technische Universität München) ;
Menze, Bjoern (Department of Computer Science. Technische Universität München) ;
Khader, Firas (ARISTRA GmbH) ;
Haarburger, Christoph (ARISTRA GmbH) ;
Scannell, Cian M. (School of Biomedical Engineering and Imaging Sciences. King's College London) ;
Veta, Mitko (Department of Biomedical Engineering. Eindhoven University of Technology) ;
Carscadden, Adam (Department of Radiology and Diagnostic Imaging. University of Alberta) ;
Punithakumar, Kumaradevan (Department of Radiology and Diagnostic Imaging. University of Alberta) ;
Liu, Xiao (School of Engineering. The University of Edinburgh) ;
Tsaftaris, Sotirios A. (School of Engineering. The University of Edinburgh) ;
Huang, Xiaoqiong (School of Biomedical Engineering. Shenzhen University) ;
Yang, Xin (School of Biomedical Engineering. Shenzhen University) ;
Li, Lei (School of Biomedical Engineering. Shenzhen University) ;
Zhuang, Xiahai (School of Data Science. Fudan University) ;
Viladés Medel, David (Institut d'Investigació Biomèdica Sant Pau) ;
Descalzo, Martin (Institut d'Investigació Biomèdica Sant Pau) ;
Guala, Andrea (Hospital Universitari Vall d'Hebron. Institut de Recerca) ;
Mura, Lucía La (Department of Advanced Biomedical Sciences. University of Naples Federico II) ;
Friedrich, Matthias G. (Department of Medicine and Diagnostic Radiology. McGill University) ;
Garg, Ria (Department of Medicine and Diagnostic Radiology. McGill University) ;
Lebel, Julie (Department of Medicine and Diagnostic Radiology. McGill University) ;
Henriques, Filipe. (Department of Cardiology. University Heart Vascular Center Hamburg) ;
Karakas, Mahir (Department of Cardiology. University Heart Vascular Center Hamburg) ;
Cavus, Ersin (Barts Heart Centre. Barts Health NHS Trust) ;
Petersen, Steffen E. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Escalera, Sergio (Hospital Universitari Vall d'Hebron. Institut de Recerca) ;
Segui, Santi (Hospital Universitari Vall d'Hebron. Institut de Recerca) ;
Rodriguez-Palomares, Jose F.. (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Lekadir, Karim (Universitat de Barcelona. Departament de Matemàtiques i Informàtica) ;
Universitat Autònoma de Barcelona
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. [...]
2021 -  10.1109/TMI.2021.3090082
IEEE Transactions on Medical Imaging, Vol. 40 Núm. 12 (january 2021) , p. 3543-3554
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4 p, 186.9 KB |
Statistical Machine Learning for Human Behaviour Analysis
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Moeslund, Thomas B. (Aalborg University. Visual Analysis of People Laboratory) ;
Escalera, Sergio (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Anbarjafari, Gholamreza (Hasan Kalyoncu University. Department of Electrical and Electronic Engineering) ;
Nasrollahi, Kamal (Aalborg University. Visual Analysis of People Laboratory) ;
Wan, Jun (Chinese Academy of Sciences. National Laboratory of Pattern Recognition)
2020 - 10.3390/e22050530
Entropy, Vol. 22, Issue 5 (May 2020) , art. 530
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15 p, 2.5 MB |
Organ Segmentation in Poultry Viscera Using RGB-D
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Philipsen, Mark Philip (Media Technology, Aalborg University) ;
Dueholm, Jacob Velling (Media Technology, Aalborg University) ;
Jørgensen, Anders (IHFood, Copenhagen) ;
Escalera, Sergio (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Moeslund, Thomas Baltzer (Media Technology, Aalborg University) ;
Universitat Autònoma de Barcelona
We present a pattern recognition framework for semantic segmentation of visual structures, that is, multi-class labelling at pixel level, and apply it to the task of segmenting organs in the eviscerated viscera from slaughtered poultry in RGB-D images. [...]
2018 - 10.3390/s18010117
Sensors (Basel, Switzerland), Vol. 18 (january 2018)
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18 p, 1.5 MB |
Social network extraction and analysis based on multimodal dyadic interaction
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Escalera, Sergio (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Baró Solé, Xavier, (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Vitrià i Marca, Jordi (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Radeva Ivanova, Petia (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Raducanu, Bogdan (Centre de Visió per Computador (Bellaterra, Catalunya))
Social interactions are a very important component in people's lives. Social network analysis has become a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from multimodal dyadic interactions. [...]
2012 - 10.3390/s120201702
Sensors (Basel, Switzerland), Vol. 12, issue 2 (Feb. 2012) , p. 1702-1719
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18 p, 1.5 MB |
GrabCut-Based Human Segmentation in Video Sequences
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Hernandez-Vela, Antonio (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Reyes, Miguel (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Ponce, Victor (Centre de Visió per Computador (Bellaterra, Catalunya)) ;
Escalera, Sergio (Centre de Visió per Computador (Bellaterra, Catalunya))
In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. [...]
2012 - 10.3390/s121115376
Sensors (Basel, Switzerland), Vol. 12 (2012) , p. 15376-15393
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22 p, 843.0 KB |
A Survey on Model Based Approaches for 2D and 3D Visual Human Pose Recovery
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Perez Sala, Xavier (Universitat Politècnica de Catalunya) ;
Escalera, Sergio (Universitat de Barcelona. Departament de Matemàtiques) ;
Angulo, Cecilio (Universitat Politècnica de Catalunya) ;
Gonzàlez, Jordi (Universitat Autònoma de Barcelona. Departament de Ciències de la Computació)
Human Pose Recovery has been studied in the field of Computer Vision forthe last 40 years. Several approaches have been reported, and significant improvements have been obtained in both data representation and model design. [...]
Pose Recovery, which is composed of five main modules: appearance, viewpoint, spatial relations, temporal consistence, and behavior. Subsequently, a methodological comparison is performed following the proposed taxonomy, evaluating current SoA approaches in the aforementioned five group categories. [...]
2014 - 10.3390/s140304189
Sensors (Basel, Switzerland), Vol. 14 (2014) , p. 4189-4210
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2 p, 289.9 KB |
"Blurred Shape Model" : innovador reconeixement automàtic d'objectes
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Escalera, Sergio (Centre de Visió per Computador (Bellaterra, Catalunya))
Una de les dificultats que trobem al reconeixement automàtic d'imatges prové de les alteracions que sofreixen els objectes en la seva representació. El fonament dels actuals sistemes intel·ligents consisteix a extraure informació rellevant de l'objecte, com podria ser el seu contorn, i aprendre que certes combinacions d'informació corresponen a objectes determinats. [...]
Una de las dificultades que encontramos en el reconocimiento automático de imágenes proviene de las alteraciones que sufren los objetos en su representación. El fundamento de los actuales sistemas inteligentes consiste en extraer información relevante del objeto, como podría ser su contorno, y aprender que ciertas combinaciones de información corresponden a objetos determinados. [...]
2010
UAB divulga, Març 2010, p. 1-2
2 documentos
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