Region growing using superpixels with learned shape prior

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Region growing using superpixels with learned shape prior

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Region growing using superpixels with learned shape prior

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA14-21421S" target="_blank" >GA14-21421S: Automatická analýza prostorových vzorů genové exprese</a><br>

Návaznosti

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

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Journal of Electronic Imaging

ISSN

1017-9909

e-ISSN

1560-229X

Svazek periodika

26

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

—

Kód UT WoS článku

000419961800013

EID výsledku v databázi Scopus

2-s2.0-85034958412