Stationarity testing in 2D image analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F19%3A43919476" target="_blank" >RIV/60461373:22340/19:43919476 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/19:00336888

Výsledek na webu

<a href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11137/2529346/Stationarity-testing-in-2D-image-analysis/10.1117/12.2529346.short" target="_blank" >https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11137/2529346/Stationarity-testing-in-2D-image-analysis/10.1117/12.2529346.short</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.2529346" target="_blank" >10.1117/12.2529346</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Stationarity testing in 2D image analysis

Popis výsledku v původním jazyce

Signal and image stationarity is the basic assumption for many methods of their analysis. However this assumption is not true in a lot of real cases. The paper is focused on local stationary testing using a small symmetric neighbourhood. The neighbourhood is split into two parts which should have the same statistical properties when the hypothesis of image stationarity is valid. We apply various testing approaches (two-sampled F-test, t-test, WMW, K-S) to obtain adequate p-values for given pixel, mask position, and test type. Finally, using battery of masks and tests, we obtain the series of p-values for every pixel. Applying False Discovery Rate (FDR) methodology, we localize all the pixels when any hypothesis falls. Resulting binary image is an alternative to traditional edge detection but with strong statistical background.

Název v anglickém jazyce

Stationarity testing in 2D image analysis

Popis výsledku anglicky

Signal and image stationarity is the basic assumption for many methods of their analysis. However this assumption is not true in a lot of real cases. The paper is focused on local stationary testing using a small symmetric neighbourhood. The neighbourhood is split into two parts which should have the same statistical properties when the hypothesis of image stationarity is valid. We apply various testing approaches (two-sampled F-test, t-test, WMW, K-S) to obtain adequate p-values for given pixel, mask position, and test type. Finally, using battery of masks and tests, we obtain the series of p-values for every pixel. Applying False Discovery Rate (FDR) methodology, we localize all the pixels when any hypothesis falls. Resulting binary image is an alternative to traditional edge detection but with strong statistical background.

Druh

D - Stať ve sborníku

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/GA17-05840S" target="_blank" >GA17-05840S: Multikriteriální optimalizace modelů prostorově variantních zobrazovacích systémů</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of SPIE Volume 11137, Applications of Digital Image Processing XLII

ISBN

978-1-5106-2967-7

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

"111372G-1"-"111372G-8"

Název nakladatele

SPIE the International Society for Optical Engineering

Místo vydání

Bellingham, WA

Místo konání akce

San Diego

Datum konání akce

11. 8. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000511298300068