Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology
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...
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doaj-ee21c780992a4ab6bb8b7f162b3f23332021-04-22T23:00:15ZengMDPI AGApplied Sciences2076-34172021-04-01113763376310.3390/app11093763Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian MorphologyYunlong Zou0Jinyu Zhao1Yuanhao Wu2Bin Wang3Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaSpace 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.https://www.mdpi.com/2076-3417/11/9/3763star image segmentationcorrelation thresholding1D Gaussian morphologycomplex backgrounds |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yunlong Zou Jinyu Zhao Yuanhao Wu Bin Wang |
spellingShingle |
Yunlong Zou Jinyu Zhao Yuanhao Wu Bin Wang Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology Applied Sciences star image segmentation correlation thresholding 1D Gaussian morphology complex backgrounds |
author_facet |
Yunlong Zou Jinyu Zhao Yuanhao Wu Bin Wang |
author_sort |
Yunlong Zou |
title |
Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology |
title_short |
Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology |
title_full |
Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology |
title_fullStr |
Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology |
title_full_unstemmed |
Segmenting Star Images with Complex Backgrounds Based on Correlation between Objects and 1D Gaussian Morphology |
title_sort |
segmenting star images with complex backgrounds based on correlation between objects and 1d gaussian morphology |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-04-01 |
description |
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. |
topic |
star image segmentation correlation thresholding 1D Gaussian morphology complex backgrounds |
url |
https://www.mdpi.com/2076-3417/11/9/3763 |
work_keys_str_mv |
AT yunlongzou segmentingstarimageswithcomplexbackgroundsbasedoncorrelationbetweenobjectsand1dgaussianmorphology AT jinyuzhao segmentingstarimageswithcomplexbackgroundsbasedoncorrelationbetweenobjectsand1dgaussianmorphology AT yuanhaowu segmentingstarimageswithcomplexbackgroundsbasedoncorrelationbetweenobjectsand1dgaussianmorphology AT binwang segmentingstarimageswithcomplexbackgroundsbasedoncorrelationbetweenobjectsand1dgaussianmorphology |
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