This HTML5 document contains 45 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n14http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n18http://localhost/temp/predkladatel/
n17http://purl.org/net/nknouf/ns/bibtex#
n12http://linked.opendata.cz/resource/domain/vavai/projekt/
n7http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n20http://linked.opendata.cz/resource/domain/vavai/subjekt/
n19http://linked.opendata.cz/ontology/domain/vavai/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
rdfshttp://www.w3.org/2000/01/rdf-schema#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/
n11http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F68407700%3A21230%2F13%3A00212551%21RIV14-GA0-21230___/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n16http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n15http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n22http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n5http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n13http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212551%21RIV14-GA0-21230___
rdf:type
skos:Concept n19:Vysledek
rdfs:seeAlso
http://www.amostech.com/TechnicalPapers/2013/POSTER/SARA.pdf
dcterms:description
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 star background, 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 does not 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 RANSAC robust method modified for a concurrent detection of a fixed number of tracks, followed by an acceptance test based on a maximum posterior probability classifier. The statistical model of an image primitive track is based on the consistence between the size and the inclination angle of the image primitive, its image motion velocity, and the sidereal velocity, together with a consistence in relative magnitude. The method does not presume any particular movements of the object, as long as its motion velocity is constant. It can detect tracks without any constraints on their angular direction or length. The detection does not require repeated image transformations (rotations etc.), which makes it computationally efficient. The detection time is linear in the number of input images and, unlike in the LINE proposal method, the number of RANSAC proposals is (theoretically) independent of the number of putative image primitives. The current (unoptimized) experimental implementation run 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 star background, 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 does not 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 RANSAC robust method modified for a concurrent detection of a fixed number of tracks, followed by an acceptance test based on a maximum posterior probability classifier. The statistical model of an image primitive track is based on the consistence between the size and the inclination angle of the image primitive, its image motion velocity, and the sidereal velocity, together with a consistence in relative magnitude. The method does not presume any particular movements of the object, as long as its motion velocity is constant. It can detect tracks without any constraints on their angular direction or length. The detection does not require repeated image transformations (rotations etc.), which makes it computationally efficient. The detection time is linear in the number of input images and, unlike in the LINE proposal method, the number of RANSAC proposals is (theoretically) independent of the number of putative image primitives. The current (unoptimized) experimental implementation run
dcterms:title
RANSACing Optical Image Sequences for GEO and near-GEO Objects RANSACing Optical Image Sequences for GEO and near-GEO Objects
skos:prefLabel
RANSACing Optical Image Sequences for GEO and near-GEO Objects RANSACing Optical Image Sequences for GEO and near-GEO Objects
skos:notation
RIV/68407700:21230/13:00212551!RIV14-GA0-21230___
n19:predkladatel
n20:orjk%3A21230
n4:aktivita
n9:P
n4:aktivity
P(GAP103/12/1578)
n4:dodaniDat
n13:2014
n4:domaciTvurceVysledku
n7:4536975 n7:8930112 n7:2053586
n4:druhVysledku
n5:D
n4:duvernostUdaju
n15:S
n4:entitaPredkladatele
n11:predkladatel
n4:idSjednocenehoVysledku
101466
n4:idVysledku
RIV/68407700:21230/13:00212551
n4:jazykVysledku
n22:eng
n4:klicovaSlova
Orbital debris; GEO objects; detection
n4:klicoveSlovo
n16:GEO%20objects n16:detection n16:Orbital%20debris
n4:kontrolniKodProRIV
[782BBCFFC271]
n4:mistoKonaniAkce
Maui
n4:mistoVydani
Kihei
n4:nazevZdroje
Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference
n4:obor
n6:JD
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n12:GAP103%2F12%2F1578
n4:rokUplatneniVysledku
n13:2013
n4:tvurceVysledku
Franc, Vojtěch Šára, Radim Matoušek, Martin
n4:typAkce
n14:WRD
n4:zahajeniAkce
2013-09-10+02:00
s:issn
2152-4629
s:numberOfPages
10
n17:hasPublisher
Maui Economic Development Board
n18:organizacniJednotka
21230