Stellar objects identification using wide-field camera

Janout, Petr; Páta, Petr; Bednář, Jan; Anisimova, Elena; Błażek, M.; Skala, P.

doi:10.1117/12.2074255

Cited by 5 publications

(4 citation statements)

References 2 publications

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“…We tested our proposed algorithm on images taken by WILLIAM (Janout et al, ) system located at Jarosov in southern Bohemia region. This system uses a digital camera (Nikon D5100) with APS‐C chip (23.6 × 15.6 mm) with resolution 4,928 × 3,264 pixels.…”

Section: Resultsmentioning

confidence: 99%

Toward astrometric calibration of ultrawide‐field images

2018

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In this paper, we introduce a new method of astrometric calibration tailored especially for ultrawide‐field systems like WILLIAM. The standard astrometric solution often fails to provide any result if the processed image contains a large amount of distortion, such as from a fisheye lens. Some algorithms yield the result, but the accuracy is satisfactory only at the center of the field of view. This work analyzes such images and proposes a new method that leads to much higher accuracy and success ratio of the calibration.

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Section: Resultsmentioning

confidence: 99%

Toward astrometric calibration of ultrawide‐field images

2018

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“…The original idea of the WILLIAM system was to create a completely autonomous ground-based all-sky detecting system, which would monitor the night sky and would detect visible stellar objects and variable stars [18]. We then aimed to evaluate weather conditions in real-time as a support experiment for autonomous robotic telescopes.…”

Section: The William Imaging Systemmentioning

confidence: 99%

Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images

et al. 2020

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All-sky imaging systems are currently very popular. They are used in ground-based meteorological stations and as a crucial part of the weather monitors for autonomous robotic telescopes. Data from all-sky imaging cameras provide important information for controlling meteorological stations and telescopes, and they have specific characteristics different from widely-used imaging systems. A particularly promising and useful application of all-sky cameras is for remote sensing of cloud cover. Post-processing of the image data obtained from all-sky imaging cameras for automatic cloud detection and for cloud classification is a very demanding task. Accurate and rapid cloud detection can provide a good way to forecast weather events such as torrential rainfalls. However, the algorithms that are used must be specifically calibrated on data from the all-sky camera in order to set up an automatic cloud detection system. This paper presents an assessment of a modified k-means++ color-based segmentation algorithm specifically adjusted to the WILLIAM (WIde-field aLL-sky Image Analyzing Monitoring system) ground-based remote all-sky imaging system for cloud detection. The segmentation method is assessed in two different color-spaces (L*a*b and XYZ). Moreover, the proposed algorithm is tested on our public WMD database (WILLIAM Meteo Database) of annotated all-sky image data, which was created specifically for testing purposes. The WMD database is available for public use. In this paper, we present a comparison of selected color-spaces and assess their suitability for the cloud color segmentation based on all-sky images. In addition, we investigate the distribution of the segmented cloud phenomena present on the all-sky images based on the color-spaces channels. In the last part of this work, we propose and discuss the possible exploitation of the color-based k-means++ segmentation method as a preprocessing step towards cloud classification in all-sky images.

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“…The proposed approach is designed for images obtained from automatic systems BOOTES [18], MAIA [19], WILLIAM [20], and GLORIA [1]. BOOTES (Burst Observer Optical Transient Exploring System) is the first Spanish robotic telescope and it is a unique system with ambitious target continuous sky observation.…”

Section: Image Datamentioning

confidence: 99%

“…The ultrawide field systems have a point spread function different from the ideal Moffat fit. The space variant approach is necessary to use for such systems [20]. The point spread function of these systems has different shape from the Moffat and Gauss approximation.…”

Section: Image Distortion Analysismentioning

confidence: 99%

The Impact of KLT Coder on the Image Distortion in Astronomy

Páta

2015

Advances in Astronomy

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Presented paper is devoted to the application of Karhunen-Loève transform (KLT) for compression and to study of KLT impact on the image distortion in astronomy. This transform is an optimal fit for images with Gaussian probability density function in order to minimize the root mean square error (RMSE). The main part of the encoder is proposed in relation to statistical image properties. Selected astronomical image processing algorithms are used for the encoder testing. The astrometry and point spread function distortion are selected as the most important criteria. The results are compared with JPEG2000 standard. The KLT encoder provides better results from the RMSE point of view. These results are promising and show the novel approach to the design of lossy image compression algorithms and also suitability for algorithms of image data structuring for retrieving, transfer, and distribution.

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Stellar objects identification using wide-field camera

Cited by 5 publications

References 2 publications

Toward astrometric calibration of ultrawide‐field images

Toward astrometric calibration of ultrawide‐field images

Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images

The Impact of KLT Coder on the Image Distortion in Astronomy

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