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Robust Underwater Image Stitching Based on Graph Matching

  • Xu Yang
  • Zhi-Yong LiuEmail author
  • Chuan Li
  • Jing-Jing Wang
  • Hong Qiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10559)

Abstract

Image stitching is important in intelligent perception and manipulation of underwater robots. In spite of a well developed technique, it is still challenging for underwater images because of their inevitable appearance ambiguity. For the feature based underwater image stitching, robust feature correspondence is the key because most other algorithmic parts are less directly associated with the characteristics of underwater images. Structural information between feature points may be helpful for robust feature correspondence, and based on this idea the paper proposes a robust underwater image stitching method by incorporating structural cues as additional information, whose effectiveness is validated on real underwater images. Specifically, the appearance information and structural cues are integrated by a labeled weighted graph, and the underwater image correspondence is formulated by graph matching. After geometric transformation estimation, the underwater images are finally blended into a wider viewing image.

Keywords

Underwater image Image stitching Feature correspondence Graph matching Structural information 

Notes

Acknowledgment

This work is supported partly by the National Natural Science Foundation (NSFC) of China (grants 61503383, 61633009, U1613213, 61375005, 61210009, and 61773047), partly by the National Key Research and Development Plan of China (grant 2016YFC0300801), partly by the Beijing Municipal Science and Technology (grants D16110400140000 and D161100001416001), and Guangdong Science and Technology Department (grant 2016B090910001).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Xu Yang
    • 1
  • Zhi-Yong Liu
    • 1
    • 2
    • 3
    Email author
  • Chuan Li
    • 1
    • 4
  • Jing-Jing Wang
    • 1
  • Hong Qiao
    • 1
    • 2
    • 3
  1. 1.State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingPeople’s Republic of China
  2. 2.Center for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghaiPeople’s Republic of China
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Research Institute of ChengduBeijing Jingdong Century Trade Co., Ltd.ChengduChina

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