Probabilistic Tracking
Probabilistic Tracking is a computational framework within cell tracking systems that models the uncertainty in cell motion and identity over time by assigning probabilities to possible trajectories, distinguishing itself from deterministic approaches through its explicit representation of tracking ambiguity using statistical inference.
232
Publications
13.6K
Citations
718
Authors
308
Institutions
Publications per year
2017–2026
44
232
Dipy, a library for the analysis of diffusion MRI data
Eleftherios Garyfallidis, Matthew Brett, Bagrat Amirbekian et al. · Frontiers in Neuroinformatics · 2014 · 1.5K citations · Full text
Medicine, Neurobiology, Neuroscience +17
Data Fusion for Visual Tracking With Particles
Patrick Pérez, J. Vermaak, Andrew Blake · Proceedings of the IEEE · 2004 · 467 citations
Computing, Physics, Computer Vision +11
1–5 of 232
718
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308
Heidelberg University
Heidelberg, Germany
10 h-index
50 publications
3K citations
Erasmus MC
Rotterdam, The Netherlands
8 h-index
36 publications
2.3K citations
Naval Undersea Warfare Center
Newport, United States
7 h-index
16 publications
695 citations

Centre National de la Recherche Scientifique
Paris, France
6 h-index
11 publications
1.2K citations
University of Cambridge
Cambridge, United Kingdom
5 h-index
6 publications
2.1K citations

Delft University of Technology
Delft, The Netherlands
4 h-index
7 publications
336 citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
12 publications
480 citations

Imperial College London
London, United Kingdom
4 h-index
15 publications
789 citations
1–8 of 308
9 publications · 409 citations
9
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7 publications · 1.1K citations
7
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7 publications · 498 citations
7
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6 publications · 371 citations
6
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5 publications · 220 citations
5
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1–5