RANSACing Optical Image Sequences for GEO and near-GEO Objects

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212551" target="_blank" >RIV/68407700:21230/13:00212551 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.amostech.com/TechnicalPapers/2013/POSTER/SARA.pdf" target="_blank" >http://www.amostech.com/TechnicalPapers/2013/POSTER/SARA.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

RANSACing Optical Image Sequences for GEO and near-GEO Objects

Popis výsledku v původním jazyce

This paper describes statistical models and an efficient Monte-Carlo algorithm for detecting tracks of slowly moving objects in optical telescope imagery sequences. The algorithm is based on accurate robust image pre-registration with respect to the starbackground, hot/warm pixel suppression, extracting dense normalized local image features, pixelwise statistical event detection, segmentation of event maps to putative image primitives, and finding consistent track sequences composed of the image primitives. Good performance at low SNR and robustness of detection with respect to fast or slow-moving thin overhead clouds is achieved by an event detection model which requires collecting at least 10 images of a particular spatial direction. The method doesnot degrade due to an accumulation of acquisition artifacts if more images are available. The track sequence detection method is similar in spirit to LINE [Yanagisawa et al, T JPN SOC AERONAUT S 2012]. The detection is performed by the R

Název v anglickém jazyce

RANSACing Optical Image Sequences for GEO and near-GEO Objects

Popis výsledku anglicky

This paper describes statistical models and an efficient Monte-Carlo algorithm for detecting tracks of slowly moving objects in optical telescope imagery sequences. The algorithm is based on accurate robust image pre-registration with respect to the starbackground, hot/warm pixel suppression, extracting dense normalized local image features, pixelwise statistical event detection, segmentation of event maps to putative image primitives, and finding consistent track sequences composed of the image primitives. Good performance at low SNR and robustness of detection with respect to fast or slow-moving thin overhead clouds is achieved by an event detection model which requires collecting at least 10 images of a particular spatial direction. The method doesnot degrade due to an accumulation of acquisition artifacts if more images are available. The track sequence detection method is similar in spirit to LINE [Yanagisawa et al, T JPN SOC AERONAUT S 2012]. The detection is performed by the R

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/GAP103%2F12%2F1578" target="_blank" >GAP103/12/1578: Strukturní a sémantické modelování architektury jako problém interpretace digitálního obrazu</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference

ISBN

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ISSN

2152-4629

e-ISSN

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Počet stran výsledku

10

Strana od-do

924-933

Název nakladatele

Maui Economic Development Board

Místo vydání

Kihei

Místo konání akce

Maui

Datum konání akce

10. 9. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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