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Kevin Smith
Kevin Smith
Associate Professor, KTH Royal Institute of Technology & Science for Life Laboratory
Dirección de correo verificada de kth.se - Página principal
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SLIC superpixels compared to state-of-the-art superpixel methods
R Achanta, A Shaji, K Smith, A Lucchi, P Fua, S Süsstrunk
IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11), 2274 …, 2012
9204*2012
Using particles to track varying numbers of interacting people
K Smith, D Gatica-Perez, JM Odobez
Computer Vision and Pattern Recognition (CVPR) 1, 962-969, 2005
2952005
Supervoxel-based segmentation of mitochondria in EM image stacks with learned shape features
A Lucchi, K Smith, R Achanta, G Knott, P Fua
IEEE Transactions on Medical Imaging 31 (2), 474-486, 2012
2802012
Evaluating multi-object tracking
K Smith, D Gatica-Perez, JM Odobez, S Ba
Computer Vision and Pattern Recognition (CVPR)-Workshops, 36-36, 2005
2122005
Slic superpixels
A Radhakrishna, A Shaji, K Smith, A Lucchi, P Fua, S Susstrunk
Dept. School Comput. Commun. Sci., EPFL, Lausanne, Switzerland, Tech. Rep 149300, 2010
211*2010
Digital image analysis in breast pathology – from image processing techniques to artificial intelligence
S Robertson, H Azizpour, K Smith, J Hartman
Translational Research 194, 19-35, 2018
1892018
Tracking the visual focus of attention for a varying number of wandering people
K Smith, SO Ba, JM Odobez, D Gatica-Perez
IEEE transactions on pattern analysis and machine intelligence 30 (7), 1212-1229, 2008
1752008
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
M Teye, H Azizpour, K Smith
International Conference on Machine Learning (ICML), 2018
1732018
Detecting abandoned luggage items in a public space
K Smith, P Quelhas, D Gatica-Perez
Computer Vision and Pattern Recognition (CVPR)-Workshops, 2006
1382006
General Constraints for Batch Multiple-Target Tracking Applied to Large-Scale Videomicroscopy
K Smith, V Lepetit, A Carleton
Computer Vision and Pattern Recognition (CVPR), 2008
131*2008
A fully automated approach to segmentation of irregularly shaped cellular structures in EM images
A Lucchi, K Smith, R Achanta, V Lepetit, P Fua
MICCAI - International Conference on Medical Image Computing and Computer …, 2010
1302010
Deep learning is combined with massive-scale citizen science to improve large-scale image classification
DP Sullivan, CF Winsnes, L Åkesson, M Hjelmare, M Wiking, R Schutten, ...
Nature biotechnology 36 (9), 820-828, 2018
1292018
CIDRE: an illumination-correction method for optical microscopy
K Smith, Y Li, F Piccinini, G Csucs, C Balazs, A Bevilacqua, P Horvath
Nature Methods 12 (5), 404-406, 2015
1152015
nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer
R Hollandi, A Szkalisity, T Toth, E Tasnadi, C Molnar, B Mathe, I Grexa, ...
Cell Systems 4 (003), 2020
99*2020
Structured image segmentation using kernelized features
A Lucchi, Y Li, K Smith, P Fua
European Conference on Computer Vision (ECCV), 400-413, 2012
832012
Fast ray features for learning irregular shapes
K Smith, A Carleton, V Lepetit
Computer Vision and Pattern Recognition (CVPR), 397-404, 2009
792009
Are spatial and global constraints really necessary for segmentation?
A Lucchi, Y Li, X Boix, K Smith, P Fua
International Conference on Computer Vision (ICCV), 9-16, 2011
782011
External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms
M Salim, E Wåhlin, K Dembrower, E Azavedo, T Foukakis, Y Liu, K Smith, ...
JAMA Oncology, 2020
732020
Phenotypic image analysis software tools for exploring and understanding big image data from cell-based assays
K Smith, F Piccinini, T Balassa, K Koos, T Danka, H Azizpour, P Horvath
Cell systems 6 (6), 636-653, 2018
612018
Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction
K Dembrower, Y Liu, H Azizpour, M Eklund, K Smith, P Lindholm, F Strand
Radiology 294 (2), 265-272, 2020
592020
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Artículos 1–20