Multiple Object Segmentation and Tracking by Bayes Risk Minimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00303601" target="_blank" >RIV/68407700:21230/16:00303601 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-46723-8_70" target="_blank" >http://dx.doi.org/10.1007/978-3-319-46723-8_70</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-46723-8_70" target="_blank" >10.1007/978-3-319-46723-8_70</a>
Alternative languages
Result language
angličtina
Original language name
Multiple Object Segmentation and Tracking by Bayes Risk Minimization
Original language description
Motion analysis of cells and subcellular particles like vesicles, microtubules or membrane receptors is essential for understanding various processes, which take place in living tissue. Manual detection and tracking is usually infeasible due to large number of particles. In addition the images are often distorted by noise caused by limited resolution of optical microscopes, which makes the analysis even more challenging. In this paper we formulate the task of detection and tracking of small objects as a Bayes risk minimization. We introduce a novel spatio-temporal probabilistic graphical model which models the dynamics of individual particles as well as their relations and propose a loss function suitable for this task. Performance of our method is evaluated on artificial but highly realistic data from the 2012 ISBI Particle Tracking Challenge [8]. We show that our approach is fully comparable or even outperforms state-of-the-art methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA16-05872S" target="_blank" >GA16-05872S: Probabilistic Graphical Models and Deep Learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, Part II
ISBN
978-3-319-46722-1
ISSN
0302-9743
e-ISSN
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Number of pages
9
Pages from-to
607-615
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Athens
Event date
Oct 17, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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