A performance evaluation of statistical tests for edge detection in textured images

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F14%3A00075212" target="_blank" >RIV/00216224:14330/14:00075212 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cviu.2014.02.009" target="_blank" >http://dx.doi.org/10.1016/j.cviu.2014.02.009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cviu.2014.02.009" target="_blank" >10.1016/j.cviu.2014.02.009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A performance evaluation of statistical tests for edge detection in textured images

Popis výsledku v původním jazyce

This work presents an objective performance analysis of statistical tests for edge detection which are suitable for textured or cluttered images. The tests are subdivided into two-sample parametric and non-parametric tests and are applied using a dual-region based edge detector which analyses local image texture difference. Through a series of experimental tests objective results are presented across a comprehensive dataset of images using a Pixel Correspondence Metric (PCM). The results show that statistical tests can in many cases, outperform the Canny edge detection method giving robust edge detection, accurate edge localisation and improved edge connectivity throughout. A visual comparison of the tests is also presented using representative imagestaken from typical textured histological data sets.

Název v anglickém jazyce

A performance evaluation of statistical tests for edge detection in textured images

Popis výsledku anglicky

This work presents an objective performance analysis of statistical tests for edge detection which are suitable for textured or cluttered images. The tests are subdivided into two-sample parametric and non-parametric tests and are applied using a dual-region based edge detector which analyses local image texture difference. Through a series of experimental tests objective results are presented across a comprehensive dataset of images using a Pixel Correspondence Metric (PCM). The results show that statistical tests can in many cases, outperform the Canny edge detection method giving robust edge detection, accurate edge localisation and improved edge connectivity throughout. A visual comparison of the tests is also presented using representative imagestaken from typical textured histological data sets.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Computer Vision and Image Understanding

ISSN

1077-3142

e-ISSN

—

Svazek periodika

122

Číslo periodika v rámci svazku

May 2014

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

115-130

Kód UT WoS článku

000334394900011

EID výsledku v databázi Scopus

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