2014
DOI: 10.3846/20296991.2014.987456
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On Reference Star Recognition and Identification

Abstract: The paper deals with a research in the area of automation of positional star observations. In order to fully employ recent progress in imaging technologies, star image recognition and reference star identification process should gain comparable level of automation. A software package for this purpose has been developed in the Institute of Geodesy of the University of Latvia. It is capable of near-real-time image processing, star identification and astrometric position determination.

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Cited by 2 publications
(3 citation statements)
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“…That ensures typically 10 to 100 stars per frame; frames with less than 10 stars occasionally can occur only when imaged area is far from galactic plane. Details of recognition and identification of star images are provided in (Zariņš et al 2014).…”
Section: Camera Designmentioning
confidence: 99%
“…That ensures typically 10 to 100 stars per frame; frames with less than 10 stars occasionally can occur only when imaged area is far from galactic plane. Details of recognition and identification of star images are provided in (Zariņš et al 2014).…”
Section: Camera Designmentioning
confidence: 99%
“…In order to successfully perform automatic star identification (software package described in (Zariņš et al 2014) was used), at least 5-6 star images should be present; bigger number of stars is desirable to increase astrometric reduction accuracy. For used hardware layout, frames obtained in a random sky area in static mode have provided necessary minimum number of images in about 80% of cases.…”
Section: Star Image Acquisition Testsmentioning
confidence: 99%
“…Real-time astrometric position availability can be quite useful for positioning system testing and calibration. It would also enable fast determination of instrument or object position for other purposes, in particular, for digital zenith camera's support (Abele et al 2012;Zariņš et al 2014).…”
Section: Introductionmentioning
confidence: 99%