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| 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 (en)
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| Title
| - RANSACing Optical Image Sequences for GEO and near-GEO Objects
- RANSACing Optical Image Sequences for GEO and near-GEO Objects (en)
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| skos:prefLabel
| - RANSACing Optical Image Sequences for GEO and near-GEO Objects
- RANSACing Optical Image Sequences for GEO and near-GEO Objects (en)
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| skos:notation
| - RIV/68407700:21230/13:00212551!RIV14-GA0-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/13:00212551
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Orbital debris; GEO objects; detection (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Šára, Radim
- Franc, Vojtěch
- Matoušek, Martin
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| issn
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| number of pages
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| http://purl.org/ne...btex#hasPublisher
| - Maui Economic Development Board
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| http://localhost/t...ganizacniJednotka
| |
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