Region growing using superpixels with learned shape prior
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00314549" target="_blank" >RIV/68407700:21230/17:00314549 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/borovec/Borovec-JEI2017-RG2Sp.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/borovec/Borovec-JEI2017-RG2Sp.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1117/1.JEI.26.6.061611" target="_blank" >10.1117/1.JEI.26.6.061611</a>
Alternative languages
Result language
angličtina
Original language name
Region growing using superpixels with learned shape prior
Original language description
Region growing is a classical image segmentation method based on hierarchical region aggregation using local similarity rules. Our proposed method differs from classical region growing in three important aspects. First, it works on the level of superpixels instead of pixels, which leads to a substantial speed-up. Second, our method uses learned statistical shape properties that encourage plausible shapes. In particular, we use ray features to describe the object boundary. Third, our method can segment multiple objects and ensure that the segmentations do not overlap. The problem is represented as an energy minimization and is solved either greedily or iteratively using graph cuts. We demonstrate the performance of the proposed method and compare it with alternative approaches on the task of segmenting individual eggs in microscopy images of Drosophila ovaries.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA14-21421S" target="_blank" >GA14-21421S: Automatic analysis of spatial gene expression patterns</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Electronic Imaging
ISSN
1017-9909
e-ISSN
1560-229X
Volume of the periodical
26
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
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UT code for WoS article
000419961800013
EID of the result in the Scopus database
2-s2.0-85034958412