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Wavelet moment invariants extraction of underwater laser vision image

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Abstract

Wavelet moment invariants are constructed for object recognition based on the global feature and local feature of target, which are brought for the simple background of the underwater objects, complex structure, similar form etc. These invariant features realize the multi-dimension feature extraction of local topology and invariant transform. Considering translation and scale invariant characteristics were ignored by conventional wavelet moments, some improvements were done in this paper. The cubic B-spline wavelets which are optimally localized in space-frequency and close to the forms of Li’s (or Zernike’s) polynomial moments were applied for calculating the wavelet moments. To testify superiority of the wavelet moments mentioned in this paper, generalized regression neural network (GRNN) was used to calculate the recognition rates based on wavelet invariant moments and conventional invariant moments respectively. Wavelet moments obtained 100% recognition rate for every object and the conventional moments obtained less classification rate. The result shows that wavelet moment has the ability to identify many types of objects and is suitable for laser image recognition.

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References

  1. Chua K, Arshad M R. Robotics vision-based heuristic reasoning for underwater target tracking and navigation [J]. International Journal of Advanced Robotic Systems, 2005, 2(3): 245–250.

    Google Scholar 

  2. Cufi X, Garcia R, Ridao P. An approach to visionbased station keeping for an unmanned underwater vehicle [C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. EPFL, Lausanne, Switzerland: IEEE, 2002: 799–804.

    Chapter  Google Scholar 

  3. Kinsey J C, Eustice R M, Whitcomb L L. A survey of underwater vehicle navigation: Recent advances and new chanllenges [C]//Proceedings of the 7th IFAC Conference on Maneuvering and Control of Marine Craft. Lisbon, Portugal: IFAC, 2006: 1–12.

    Google Scholar 

  4. Hu M-K. Visual pattern recognition by moment invariants [J]. IRE Transactions on Information Theory, 1962, 8(1): 179–187.

    MATH  Google Scholar 

  5. Li Y J. Reforming the theory of invariant moments for pattern recognition [J]. Pattern Recognition, 1992, 25(7): 723–730.

    Article  Google Scholar 

  6. Teague M R. Image analysis via the general theory of moments [J]. Journal of Optical Society American, 1980, 70(8): 920–930.

    Article  MathSciNet  Google Scholar 

  7. Belkasim S O, Shridhar M, Ahmadi M. Pattern recognition with moment invariants: A comparative study and new results [J]. Pattern Recognition, 1991, 24(12): 1117–1138.

    Article  Google Scholar 

  8. Tang Xu-dong, Zhu Wei, Pang Yong-jie, et al. Target recognition system based on optical vision for AUV [J]. Robot, 2009, 31(2): 171–178 (in Chinese).

    Google Scholar 

  9. Shen D, Ip H H S. Discriminative wavelet shape descriptors for recognition of 2-D patterns [J]. Pattern Recognition, 1999, 32(2): 151–165.

    Article  Google Scholar 

  10. Wang Xiang-yang, Yang Yi-ping, Yang Hong-ying. A new robust digital image watermarking based on wavelet moments [J]. Journal of Image and Graphics, 2010, 15(1): 15–19 (in Chinese).

    Google Scholar 

  11. Zhang Tie-zhu, Jiang Hong. Intelligent target recognition based on the data fusion of radar and infrared imaging sensor [J]. Infrared and Laser Engineering, 2010, 39(4): 756–760 (in Chinese).

    Google Scholar 

  12. Zhu Guang-tao, Li Ying. License plate character recognition method based on wavelet moment and principal component analysis [J]. Computer Systems Application, 2012, 21(7): 168–171 (in Chinese).

    Google Scholar 

  13. Zhang He, Xu Yu-ru, Wan Lei, et al. Processing method for underwater degenerative Image [J]. Journal of Tianjin University, 2010, 43(9): 827–833 (in Chinese).

    Google Scholar 

  14. Wang Yi-feng, Liu Zhi-shen. A denoising method for underwater laser image based on mathematical morphology [J]. Electro-Optic Technology Application, 2006, 21(3): 51–53 (in Chinese).

    Google Scholar 

  15. Erkmen B, Yildirim T. Improving classification performance of sonar targets by applying general regression neural network with PCA [J]. Expert Systems with Applications, 2008, 35(1–2): 472–475.

    Article  Google Scholar 

  16. Parojčić J, Ibrić S, Djurić Z, et al. An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro-in vivo correlation [J]. European Journal of Pharmaceutical Sciences, 2007, 30(3–4): 264–272.

    Google Scholar 

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Authors and Affiliations

  1. Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin, 150001, China

    Shu-ling Huang  (黄蜀玲), Yong-jie Pang  (庞永杰), Bo Wang  (王 博) & Lei Wan  (万 磊)

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  1. Shu-ling Huang  (黄蜀玲)
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  2. Yong-jie Pang  (庞永杰)
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  3. Bo Wang  (王 博)
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  4. Lei Wan  (万 磊)
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Corresponding author

Correspondence to Shu-ling Huang  (黄蜀玲).

Additional information

Foundation item: the Fundamental Research Funds for Central Universities (No. HEUCF110111), the National Natural Science Foundation of China (No. 51009040), the China Postdoctoral Science Foundation (No. 2012M510928), the Heilongjiang Postdoctoral Fund (No. LBH-Z11205), and the National High Technology Research and Development Program (863) of China (No. 2011AA09A106)

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Huang, Sl., Pang, Yj., Wang, B. et al. Wavelet moment invariants extraction of underwater laser vision image. J. Shanghai Jiaotong Univ. (Sci.) 18, 712–718 (2013). https://doi.org/10.1007/s12204-013-1454-6

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  • DOI: https://doi.org/10.1007/s12204-013-1454-6

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