Faint Streak Detection with Certificate by Adaptive Two-Level Bayesian Inference
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00312175" target="_blank" >RIV/68407700:21230/17:00312175 - isvavai.cz</a>
Result on the web
<a href="https://conference.sdo.esoc.esa.int/proceedings/sdc7/paper/403/SDC7-paper403.pdf" target="_blank" >https://conference.sdo.esoc.esa.int/proceedings/sdc7/paper/403/SDC7-paper403.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Faint Streak Detection with Certificate by Adaptive Two-Level Bayesian Inference
Original language description
It is known that detecting straight streaks from fast moving celestial objects in optical images is an easy problem as long as the streaks are sufficiently long and/or their signal-to-background (SBR) is sufficiently high. At low SBR the situation is different. Since the SBR can be arbitrarily small in practice, a good detection procedure has to provide a detection certificate which is a yes/no answer to the question “does the image contain a streak?” In this paper we pose detection with certificate as a Multi-Level Bayesian Inference (MLBI) problem which is based on Bayesian model selection. We describe the algorithm and show an experimental proof of good behavior on synthetic streaks over real image data. A systematic performance evaluation shows that MLBI confirms and partially exceeds results of state-of-the art methods. In particular, in the class of difficult problem instances with SBR of 0 dB to -5 dB and streak length 10 to 500 pixels, we achieved AUC approximately 0.97, which means that the Bayesian detection certificate is wrong in just 3% of cases.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů