Volume 15 Issue 3
May  2022
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KOU Peng, ZHI Shuai-feng, CHENG Yun, LIU Yong-xiang. Detection of elliptical components in adaptive optical image of space target[J]. Chinese Optics, 2022, 15(3): 454-463. doi: 10.37188/CO.2021-0208
Citation: KOU Peng, ZHI Shuai-feng, CHENG Yun, LIU Yong-xiang. Detection of elliptical components in adaptive optical image of space target[J]. Chinese Optics, 2022, 15(3): 454-463. doi: 10.37188/CO.2021-0208

Detection of elliptical components in adaptive optical image of space target

doi: 10.37188/CO.2021-0208
Funds:  Supported by National Natural Science Foundation of China (No. 61921001, No. 61801484)
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  • Corresponding author: lyx_bible@sina.com
  • Received Date: 03 Dec 2021
  • Rev Recd Date: 04 Jan 2022
  • Accepted Date: 01 Mar 2022
  • Available Online: 01 Mar 2022
  • Publish Date: 20 May 2022
  • In order to identify the elliptical components of space target, an ellipse detection method based on adaptive optical image is proposed. Firstly, the RL(Richardson-Lucy) method is used to restore the adaptive optics image. Next, the Arc-Support Line Segments (ASLS) method is used to detect the ellipse of the restored image. To tackle the problems of “arc segment over segmentation” and “semantic information difference” caused by Canny edge extraction, an improved edge extraction algorithm based on Multiscale Combinatorial Grouping (MCG) is proposed. Finally, for some false ellipses produced by using verification methods such as goodness measurement, a variety of geometric constraint measurement are comprehensively used to effectively eliminate the false ellipse. The experimental results show that the detection error of ellipse center point, the semi-major axis error and the direction angle error are less than 3 pixels, 4 pixels and 3 degrees, respectively. When the overlap area threshold is 0.65, the accuracy rate of this algorithm is 85.7%, the recall rate is 93.3% and the F value is 0.893. Our method is better than the traditional ellipse detection algorithms.

     

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