About: Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy     Goto   Sponge   NotDistinct   Permalink

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  • In the field of biomedical image analysis, motion tracking and segmentation algorithms are important tools for time-resolved analysis of cell characteristics, events, and tracking. There are many algorithms in everyday use. Nevertheless, most of them is not properly validated as the ground truth (GT), which is a very important tool for the verification of image processing algorithms, is not naturally available. Many algorithms in this field of study are, therefore, validated only manually by an human expert. This is usually difficult, cumbersome and time consuming task, especially when single 3D image or even 3D image sequence is considered. In this paper, we have proposed a technique that generates time-lapse sequences of fully 3D synthetic image datasets. It includes generating shape, structure, and also motion of selected biological objects. The corresponding GT data is generated as well. The technique is focused on the generation of synthetic objects at various scales.
  • In the field of biomedical image analysis, motion tracking and segmentation algorithms are important tools for time-resolved analysis of cell characteristics, events, and tracking. There are many algorithms in everyday use. Nevertheless, most of them is not properly validated as the ground truth (GT), which is a very important tool for the verification of image processing algorithms, is not naturally available. Many algorithms in this field of study are, therefore, validated only manually by an human expert. This is usually difficult, cumbersome and time consuming task, especially when single 3D image or even 3D image sequence is considered. In this paper, we have proposed a technique that generates time-lapse sequences of fully 3D synthetic image datasets. It includes generating shape, structure, and also motion of selected biological objects. The corresponding GT data is generated as well. The technique is focused on the generation of synthetic objects at various scales. (en)
Title
  • Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy
  • Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy (en)
skos:prefLabel
  • Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy
  • Generation of Synthetic Image Datasets for Time-Lapse Fluorescence Microscopy (en)
skos:notation
  • RIV/00216224:14330/12:00057285!RIV13-GA0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GBP302/12/G157), S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 137856
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/12:00057285
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Simulation; Optical flow; 3D image sequences; Fluorescence optical microscopy (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [ADDF703BA1EE]
http://linked.open...v/mistoKonaniAkce
  • Aveiro, Portugal
http://linked.open...i/riv/mistoVydani
  • Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Proceedings of 9th International Conference on Image Analysis and Recognition
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Svoboda, David
  • Ulman, Vladimír
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-642-31298-4_56
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 9783642312977
http://localhost/t...ganizacniJednotka
  • 14330
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