Robust underwater image stitching based on graph matching

Xu Yang; Zhi Yong Liu; Chuan Li; Jing Jing Wang; Hong Qiao

doi:10.1007/978-3-319-67777-4_46

Robust underwater image stitching based on graph matching

Xu Yang
, Zhi Yong Liu
, Chuan Li
, Jing Jing Wang
, Hong Qiao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings
Editors	Yi Sun, Huchuan Lu, Lihe Zhang, Jian Yang, Hua Huang
Publisher	Springer Verlag
Pages	521-529
Number of pages	9
ISBN (Print)	9783319677767
DOIs	https://doi.org/10.1007/978-3-319-67777-4_46
State	Published - 2017
Externally published	Yes
Event	7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017 - Dalian, China Duration: 22 Sep 2017 → 23 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10559 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017
Country/Territory	China
City	Dalian
Period	22/09/17 → 23/09/17

Keywords

Feature correspondence
Graph matching
Image stitching
Structural information
Underwater image

Access to Document

10.1007/978-3-319-67777-4_46

Cite this

Yang, X., Liu, Z. Y., Li, C., Wang, J. J., & Qiao, H. (2017). Robust underwater image stitching based on graph matching. In Y. Sun, H. Lu, L. Zhang, J. Yang, & H. Huang (Eds.), Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings (pp. 521-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10559 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67777-4_46

Yang, Xu ; Liu, Zhi Yong ; Li, Chuan et al. / Robust underwater image stitching based on graph matching. Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings. editor / Yi Sun ; Huchuan Lu ; Lihe Zhang ; Jian Yang ; Hua Huang. Springer Verlag, 2017. pp. 521-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0899ff7956e740f5a59793248ba7394e,

title = "Robust underwater image stitching based on graph matching",

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 = "Feature correspondence, Graph matching, Image stitching, Structural information, Underwater image",

author = "Xu Yang and Liu, \{Zhi Yong\} and Chuan Li and Wang, \{Jing Jing\} and Hong Qiao",

note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017 ; Conference date: 22-09-2017 Through 23-09-2017",

year = "2017",

doi = "10.1007/978-3-319-67777-4\_46",

language = "英语",

isbn = "9783319677767",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

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editor = "Yi Sun and Huchuan Lu and Lihe Zhang and Jian Yang and Hua Huang",

booktitle = "Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings",

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Yang, X, Liu, ZY, Li, C, Wang, JJ & Qiao, H 2017, Robust underwater image stitching based on graph matching. in Y Sun, H Lu, L Zhang, J Yang & H Huang (eds), Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10559 LNCS, Springer Verlag, pp. 521-529, 7th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2017, Dalian, China, 22/09/17. https://doi.org/10.1007/978-3-319-67777-4_46

Robust underwater image stitching based on graph matching. / Yang, Xu; Liu, Zhi Yong; Li, Chuan et al.
Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings. ed. / Yi Sun; Huchuan Lu; Lihe Zhang; Jian Yang; Hua Huang. Springer Verlag, 2017. p. 521-529 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10559 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Robust underwater image stitching based on graph matching

AU - Yang, Xu

AU - Liu, Zhi Yong

AU - Li, Chuan

AU - Wang, Jing Jing

AU - Qiao, Hong

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Feature correspondence

KW - Graph matching

KW - Image stitching

KW - Structural information

KW - Underwater image

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BT - Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings

A2 - Sun, Yi

A2 - Lu, Huchuan

A2 - Zhang, Lihe

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A2 - Huang, Hua

PB - Springer Verlag

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Y2 - 22 September 2017 through 23 September 2017

ER -

Yang X, Liu ZY, Li C, Wang JJ, Qiao H. Robust underwater image stitching based on graph matching. In Sun Y, Lu H, Zhang L, Yang J, Huang H, editors, Intelligence Science and Big Data Engineering - 7th International Conference, IScIDE 2017, Proceedings. Springer Verlag. 2017. p. 521-529. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-67777-4_46

Robust underwater image stitching based on graph matching

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Keywords

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