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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

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

  • 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