An improved method for object detection in astronomical images

Zheng, Caixia; Pulido, Jesus; Thorman, Paul; Hamann, Bernd

doi:10.1093/mnras/stv1237

Cited by 25 publications

(21 citation statements)

References 13 publications

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“…We can use popular algorithms to detect stars, nebulae and comets on astronomical images [13]. In our experiment we use AstroPy [9] library for FITS files from ESO archive.…”

Section: Hit Detection On Whole Image Frame and Crop Image With Hitmentioning

confidence: 99%

Recognition and classification of the cosmic-ray events in images captured by CMOS/CCD cameras

Niedźwiecki¹,

Rzecki²,

Marek³

et al. 2019

Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019)

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Muons and other ionizing radiation produced by cosmic rays and radiative decays affect CMOS/CCD sensor. When particles colliding with sensors atoms cause specific kind of noise on images recorded by cameras. We present a concept and preliminary implementation of method for recognizing those events and algorithms for image processing and their classification by machine learning. Our method consists of analyzing the shape of traces present in images recorded by a camera sensor and metadata related to an image like camera model, GPS location of camera, vertical and horizontal orientation of a camera sensor, timestamp of image acquisition, and other events recognized nearby sensors. The so created feature vectors are classified as either a muon-like event, an electron-like event or the other event, possibly noise. For muon-like events our method estimates azimuth of a muon track. Source of the data is database of CREDO (Cosmic-Ray Extremely Distributed Observatory) project and ESO (European Southern Observatory) archives. The telescope dark frames from ESO are analysed. CREDO project collected so far over 2 millions images of events from many kinds of cameralike: smartphones camera, laptop webcams and Internet of Things cameras localised around the globe.

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“…We can use popular algorithms to detect stars, nebulae and comets on astronomical images [13]. In our experiment we use AstroPy [9] library for FITS files from ESO archive.…”

Section: Hit Detection On Whole Image Frame and Crop Image With Hitmentioning

confidence: 99%

Recognition and classification of the cosmic-ray events in images captured by CMOS/CCD cameras

Niedźwiecki¹,

Rzecki²,

Marek³

et al. 2019

Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019)

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“…The choice of percentage of coe cients has the most impact on lossy compression quality. A highly e↵ective method for object detection by Zheng et al [14] is used to evaluate the quality of lossy compression and object detection at various levels. In the original data, a total of 401 objects can be extracted with this method on a 64MB FITS container.…”

Section: Astronomical Imagesmentioning

confidence: 99%

Data Reduction Using Lossy Compression for Cosmology and Astrophysics Workflows

Pulido

Lukić

Thorman

et al. 2018

Self Cite

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This paper concerns the use of compression methods applied to large scientific data. Specifically the paper addresses the e↵ect of lossy compression on approximation error. Computer simulations, experiments and imaging technologies generate terabyte-scale datasets making necessary new approaches for compression coupled with data analysis. Lossless compression techniques compress data with no loss of information, but they generally do not produce a large-enough reduction when compared to lossy compression methods. Lossy multiresolution compression techniques make it possible to compress large datasets significantly with small numerical error, preserving coherent features and statistical properties needed for analysis. Lossy data compression reduces I/O data transfer cost and makes it possible to store more data at higher temporal resolution. We present results obtained with lossy multiresolution compression, with a focus on astrophysics datasets. Our results confirm that lossy data compression is capable of preserving data characteristics very well, even at extremely high degrees of compression.

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“…Thresholding segmentation [1] is an important process for space object recognition and extraction in star images. Stars and skylight backgrounds are separated by differences in grayscale, and segmentation thresholdings commonly used today are global thresholdings [2][3][4][5][6][7][8][9][10][11][12][13] and local thresholdings [14][15][16][17][18][19]. A global thresholding is to specify a unified thresholding for all pixels of an image, such as the Otsu method [2][3][4][5], maximum entropy method [6][7][8][9][10] and the minimum error thresholding method [11], which are only applicable to star images where the gray levels of objects and backgrounds are clearly distinguished.…”

Section: Introductionmentioning

confidence: 99%

Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

Zou

Zhao

et al. 2021

Applied Sciences

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Space object recognition in high Earth orbits (between 2000 km and 36,000 km) is affected by moonlight and clouds, resulting in some bright or saturated image areas and uneven image backgrounds. It is difficult to separate dim objects from complex backgrounds with gray thresholding methods alone. In this paper, we present a segmentation method of star images with complex backgrounds based on correlation between space objects and one-dimensional (1D) Gaussian morphology, and the focus is shifted from gray thresholding to correlation thresholding. We build 1D Gaussian functions with five consecutive column data of an image as a group based on minimum mean square error rules, and the correlation coefficients between the column data and functions are used to extract objects and stars. Then, lateral correlation is repeated around the identified objects and stars to ensure their complete outlines, and false alarms are removed by setting two values, the standard deviation and the ratio of mean square error and variance. We analyze the selection process of each thresholding, and experimental results demonstrate that our proposed correlation segmentation method has obvious advantages in complex backgrounds, which is attractive for object detection and tracking on a cloudy and bright moonlit night.

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An improved method for object detection in astronomical images

Cited by 25 publications

References 13 publications

Recognition and classification of the cosmic-ray events in images captured by CMOS/CCD cameras

Recognition and classification of the cosmic-ray events in images captured by CMOS/CCD cameras

Data Reduction Using Lossy Compression for Cosmology and Astrophysics Workflows

Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology

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