Timezone: »
Poster
Structured Learning for Cell Tracking
Xinghua Lou · Fred Hamprecht
We study the problem of learning to track a large quantity of homogeneous objects such as cell tracking in cell culture study and developmental biology. Reliable cell tracking in time-lapse microscopic image sequences is important for modern biomedical research. Existing cell tracking methods are usually kept simple and use only a small number of features to allow for manual parameter tweaking or grid search. We propose a structured learning approach that allows to learn optimum parameters automatically from a training set. This allows for the use of a richer set of features which in turn affords improved tracking compared to recently reported methods on two public benchmark sequences.
Author Information
Xinghua Lou (DeepMind)
Fred Hamprecht (Heidelberg University)
More from the Same Authors
-
2022 Poster: Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources »
Peter Lippmann · Enrique Fita SanmartĂn · Fred Hamprecht -
2021 Poster: Directed Probabilistic Watershed »
Enrique Fita Sanmartin · Sebastian Damrich · Fred Hamprecht -
2021 Poster: On UMAP's True Loss Function »
Sebastian Damrich · Fred Hamprecht -
2019 Poster: Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning »
Enrique Fita Sanmartin · Sebastian Damrich · Fred Hamprecht -
2019 Spotlight: Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning »
Enrique Fita Sanmartin · Sebastian Damrich · Fred Hamprecht -
2017 Poster: Sparse convolutional coding for neuronal assembly detection »
Sven Peter · Elke Kirschbaum · Martin Both · Lee Campbell · Brandon Harvey · Conor Heins · Daniel Durstewitz · Ferran Diego · Fred Hamprecht -
2017 Poster: Cost efficient gradient boosting »
Sven Peter · Ferran Diego · Fred Hamprecht · Boaz Nadler -
2016 : Fred Hamprecht : Motif Discovery in Functional Brain Data »
Fred Hamprecht -
2016 Poster: Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data »
Xinghua Lou · Ken Kansky · Wolfgang Lehrach · CC Laan · Bhaskara Marthi · D. Phoenix · Dileep George -
2014 Poster: Sparse Space-Time Deconvolution for Calcium Image Analysis »
Ferran Diego Andilla · Fred Hamprecht -
2014 Spotlight: Sparse Space-Time Deconvolution for Calcium Image Analysis »
Ferran Diego Andilla · Fred Hamprecht -
2013 Poster: Learning Multi-level Sparse Representations »
Ferran Diego Andilla · Fred Hamprecht