Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F08%3A00042085" target="_blank" >RIV/00216224:14330/08:00042085 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks

Popis výsledku v původním jazyce

A novel edge detector has been developed that utilizes statistical masks and neural networks for the optimal detection of edges over a wide range of image types. The failure of many common edge detection techniques has been observed when analyzing concealed weapons X-ray images, biomedical images or images with significant levels of noise, clutter or texture. This novel technique is based on a statistical edge detection filter that uses a range of two-sample statistical tests to evaluate any local imagetexture differences. The range and type of tests has been greatly expanded from the previous works. This process is further enhanced by applying combined multiple scale pixel masks and multiple statistical tests, to Artificial Neural Networks (ANN) trained to classify different edge types. Through the use of Artificial Neural Networks (ANN) we can combine the output results of several statistical mask scales into one detector.

Název v anglickém jazyce

Statistical Edge Detection of Concealed Weapons Using Artificial Neural Networks

Popis výsledku anglicky

A novel edge detector has been developed that utilizes statistical masks and neural networks for the optimal detection of edges over a wide range of image types. The failure of many common edge detection techniques has been observed when analyzing concealed weapons X-ray images, biomedical images or images with significant levels of noise, clutter or texture. This novel technique is based on a statistical edge detection filter that uses a range of two-sample statistical tests to evaluate any local imagetexture differences. The range and type of tests has been greatly expanded from the previous works. This process is further enhanced by applying combined multiple scale pixel masks and multiple statistical tests, to Artificial Neural Networks (ANN) trained to classify different edge types. Through the use of Artificial Neural Networks (ANN) we can combine the output results of several statistical mask scales into one detector.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/1K05021" target="_blank" >1K05021: Rekonstrukce objektů v biomedicínských obrazech pomocí statistických metod a metod umělé inteligence</a><br>

Návaznosti

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

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of SPIE-IS&T Electronic Imaging

ISBN

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ISSN

0277-786X

e-ISSN

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Počet stran výsledku

12

Strana od-do

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Název nakladatele

SPIE

Místo vydání

Bellingham, Washington

Místo konání akce

San Jose, CA, USA

Datum konání akce

1. 1. 2008

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000256350500050