Faint Streak Detection with Certificate by Adaptive Two-Level Bayesian Inference

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

Faint Streak Detection with Certificate by Adaptive Two-Level Bayesian Inference

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Faint Streak Detection with Certificate by Adaptive Two-Level Bayesian Inference

Popis výsledku anglicky

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.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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