Multiple Object Segmentation and Tracking by Bayes Risk Minimization

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Multiple Object Segmentation and Tracking by Bayes Risk Minimization

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multiple Object Segmentation and Tracking by Bayes Risk Minimization

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA16-05872S" target="_blank" >GA16-05872S: Pravděpodobnostní grafové modely a hluboké učení</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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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Počet stran výsledku

9

Strana od-do

607-615

Název nakladatele

Springer International Publishing

Místo vydání

Cham

Místo konání akce

Athens

Datum konání akce

17. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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