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      Near Infrared Star Centroid Detection by Area Analysis of Multi-Scale Super Pixel Saliency Fusion Map

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          Abstract

          The centroid location of a near infrared star always deviates from the real center due to the effects of surrounding radiation. To determine a more accurate center of a near infrared star, this paper proposes a method to detect the star’s saliency area and calculate the star’s centroid via the pixels only in this area, which can greatly decrease the effect of the radiation. During saliency area detection, we calculated the boundary connectivity and gray similarity of every pixel to estimate how likely it was to be a background pixel. Aiming to simplify and speed up the calculation process, we divided the near infrared starry sky image into super pixel maps at multi-scale by Simple Linear Iterative Clustering (SLIC). Second, we detected the saliency map for every super pixel map of the image. Finally, we fused the saliency maps according to a weighted coefficient that is determined by the least square method. For the images used in our experiment, we set the multi-scale super pixel numbers to 100, 150, and 200. The results show that our method can obtain an offset variance of less than 0.27 for the center coordinates compared to the labelled centers.

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          Author and article information

          Journal
          Tsinghua Science and Technology
          Tsinghua Science and Technology
          Tsinghua University Press (Xueyan Building, Tsinghua University, Beijing 100084, China )
          1007-0214
          05 June 2019
          : 24
          : 3
          : 291-300
          Affiliations
          ∙ Xiaohu Yuan and Chunwen Li are with the Department of Automation, Tsinghua University, Beijing 100084, China. E-mail: lcw@tsinghua.edu.cn.
          ∙ Shaojun Guo is with the National Innovation Institute of Defense Technology, Beijing 100091, China. E-mail: guoba2000@163.com.
          ∙ Bin Lu is with Naval Aeronautical University, Yantai 264001, China. E-mail: glovz2011@163.com.
          ∙ Shuli Lou is with Yantai University, Yantai 264005, China. E-mail: shulilou@sina.com.
          Author notes
          * To whom correspondence should be addressed. E-mail: yxh96105@ 123456163.com ;

          Xiaohu Yuan received the PhD degree in control science and engineering from Tsinghua University, Beijing, China, in 2018. He is currently an engineer in the Department of Automation, Tsinghua University. His research interests include image processing and quantum information processing.

          Shaojun Guo received the PhD degree in signal and information processing from Navy Aeronautical University, Yantai, China, in 2017. He is currently an engineer of National Innovation Institute of Defense Technology. His current research interests include image processing and machine learning algorithms.

          Chunwen Li received the PhD degree in control science and engineering from Tsinghua University, Beijing, China, in 1989. He is currently a professor with the Department of Automation, Tsinghua University. His research interests include non-linear control and inverse system control.

          Bin Lu received the PhD degree in signal and information processing from Naval Areonautical Engineering Institute, Yantai, China, in 2012. He is currently an associate professor with Naval Aeronautical University. His current research interests include image processing and opto-electronic engineering.

          Shuli Lou received the PhD degree in signal and information processing from Naval Areonautical Engineering Institute, Yantai, China, in 2011. He is currently an associate professor with Yantai University. His current research interests include opto-electronic engineering and image processing.

          Article
          1007-0214-24-3-291
          10.26599/TST.2018.9010048
          Product
          Self URI: Publisher

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