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14 p, 1.7 MB |
Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis : an evaluation of four automated methods against manual reference segmentations in a multi-center cohort
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de Sitter, Alexandra (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Verhoeven, Tom (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Burggraaff, Jessica (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Neurology, MS Center Amsterdam) ;
Liu, Yaou (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Simoes, Jorge (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Ruggieri, Serena (San Camillo Forlanini Hospital. Department of Neurosciences) ;
Palotai, Miklos (Harvard Medical School. Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital) ;
Brouwer, Iman (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Versteeg, Adriaan (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Wottschel, Viktor (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Ropele, Stefan (Medical University of Graz. Department of Neurology) ;
Rocca, Mara A. (IRCCS San Raffaele Scientific Institute. Neurology Unit) ;
Gasperini, Claudio (San Camillo Forlanini Hospital. Department of Neurosciences) ;
Gallo, Antonio (University of Campania "Luigi Vanvitelli". Division of Neurology and MRI Research Center, Department of Medical, Surgical, Neurologic, Metabolic and Aging Sciences) ;
Yiannakas, Marios C. (University College London. Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology) ;
Rovira, Alex (Hospital Universitari Vall d'Hebron. Institut de Recerca) ;
Enzinger, Christian (University of Graz. Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology Medical) ;
Filippi, Massimo (Vita-Salute San Raffaele University) ;
De Stefano, Nicola (University of Siena. Department of Neurological and Behavioural Sciences) ;
Kappos, Ludwig (University Hospital, Kantonsspital. Department of Neurology) ;
Frederiksen, Jette L. (Glostrup University Hospital Copenhagen. Department of Neurology) ;
Uitdehaag, Bernard M. J. (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Neurology, MS Center Amsterdam) ;
Barkhof, Frederik (UCL London. Institutes of Neurology and Healthcare Engineering) ;
Guttmann, Charles R. G. (Harvard Medical School. Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital) ;
Vrenken, Hugo (Amsterdam Neuroscience, Amsterdam UMC, Location VUmc. Department of Radiology and Nuclear Medicine, MS Center Amsterdam) ;
Universitat Autònoma de Barcelona
Deep grey matter (DGM) atrophy in multiple sclerosis (MS) and its relation to cognitive and clinical decline requires accurate measurements. MS pathology may deteriorate the performance of automated segmentation methods. [...]
2020 - 10.1007/s00415-020-10023-1
Journal of Neurology, Vol. 267 (july 2020) , p. 3541-3554
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21 p, 1.0 MB |
Scale Invariant Mask R-CNN for Pedestrian Detection
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Gawande, Ujwalla H (Yeshwantrao Chavan College of Engineering (Maharashtra, Índia). Department of Information Technology) ;
Hajari, Kamal O (Yeshwantrao Chavan College of Engineering (Maharashtra, Índia). Department of Information Technology) ;
Golhar, Yogesh G (R. H. Raisoni College of Engineering (Maharashtra, Índia). Department of Computer Science and Engineering)
Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrianshelps in various utility applications such as event detection in overcrowded areas, gender, and gaitclassification, etc. [...]
2020 - 10.5565/rev/elcvia.1278
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 19 Núm. 3 (2020) , p. 98-118 (Regular Issue)
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15 p, 865.3 KB |
Iris recognition algorithm based on Contourlet Transform and Entropy
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Ezzaki, Ayoub (Mohammed V University in Rabat. Physics Department) ;
Idrissi, Nadia (Mohammed V University in Rabat. Physics Department) ;
Moreno Dueñas, Francisco Ángel (Universidad de Málaga. Departamento de Ingeniería de Sistemas y Automática) ;
Masmoudi, Lhoussaine (Mohammed V University in Rabat. Physics Department)
The iris is one of the most secure biometric information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Entropy which entails i) the detection and segmentation of the iris, ii) its normalization, iii) the application of the Contourlet Transform, iv) the generation of the iris descriptor, and v) the matching between the query iris and those in the database. [...]
2020 - 10.5565/rev/elcvia.1190
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 19 Núm. 1 (2020) , p. 53-67 (Regular Issue)
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13 p, 6.4 MB |
Robust computer vision system for marbling meat segmentation
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Campos, Gabriel Fillipe Centini (Universidade Estadual de Londrina. Department of Computer Science) ;
Seixas Jr., José Luis (Eötvös Loránd University (Budapest, Hongria). Department of Data Science and Engineering) ;
Barbon, Ana Paula A. C. (Universidade Estadual de Londrina. Department of Zootechnology) ;
Felinto, Alan Salvany (Universidade Estadual de Londrina. Department of Computer Science) ;
Bridi, Ana Maria (Universidade Estadual de Londrina. Department of Zootechnology) ;
Barbon Jr., Sylvio (Universidade Estadual de Londrina. Department of Computer Science)
In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment muscle fat in various marbled meat samples using images acquired with different quality devices in an uncontrolled environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of sample treatments or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. [...]
2020 - 10.5565/rev/elcvia.777
ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 19 Núm. 1 (2020) , p. 15-27 (Regular Issue)
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