Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

doi:10.1007/s12204-013-1454-6

上海交通大学学报（英文版） ›› 2013, Vol. 18 ›› Issue (6): 712-718.doi: 10.1007/s12204-013-1454-6

Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

HUANG Shu-ling* (黄蜀玲), PANG Yong-jie (庞永杰), WANG Bo (王博), WAN Lei (万磊)

(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)

出版日期:2013-12-31 发布日期:2013-12-18
通讯作者: HUANG Shu-ling (黄蜀玲) E-mail:lingqing.2009@163.com

Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

HUANG Shu-ling* (黄蜀玲), PANG Yong-jie (庞永杰), WANG Bo (王博), WAN Lei (万磊)

(Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 150001, China)

Online:2013-12-31 Published:2013-12-18
Contact: HUANG Shu-ling (黄蜀玲) E-mail:lingqing.2009@163.com

摘要/Abstract

摘要： 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.

关键词: wavelet moment invariants, underwater optical vision images, underwater laser imaging, feature extraction, object recognition

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.

Key words: wavelet moment invariants, underwater optical vision images, underwater laser imaging, feature extraction, object recognition

中图分类号:

U 674.941

HUANG Shu-ling* (黄蜀玲), PANG Yong-jie (庞永杰), WANG Bo (王博), WAN Lei (万磊). Wavelet Moment Invariants Extraction of Underwater Laser Vision Image[J]. 上海交通大学学报（英文版）, 2013, 18(6): 712-718.

HUANG Shu-ling* (黄蜀玲), PANG Yong-jie (庞永杰), WANG Bo (王博), WAN Lei (万磊). Wavelet Moment Invariants Extraction of Underwater Laser Vision Image[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(6): 712-718.

参考文献

[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.
[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.
[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.
[4] Hu M-K. Visual pattern recognition by moment invariants [J]. IRE Transactions on Information Theory,1962, 8(1): 179-187.
[5] Li Y J. Reforming the theory of invariant moments for pattern recognition [J]. Pattern Recognition, 1992,25(7): 723-730.
[6] Teague M R. Image analysis via the general theory of moments [J]. Journal of Optical Society American,1980, 70(8): 920-930.
[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.
[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).
[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.
[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).
[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).
[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).
[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).
[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).
[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.
[16] Paroj?ci′c J, Ibri′c S,Djuri′c 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.

Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 5

编辑推荐

Metrics

本文评价

[1]	WANG Yukui1,2* (王余奎), HUANG Zhijie1 (黄之杰), ZHAO Xucheng1 (赵徐成), ZHU Yi1 (朱毅), W. A Novel De-noising Method Based on Discrete Cosine Transform and Its Application in the Fault Feature Extraction of Hydraulic Pump[J]. 上海交通大学学报（英文版）, 2016, 21(3): 297-306.
[2]	LI Hong-kun1 (李宏坤), ZHANG Zhi-xin2* (张志新), LI Xiu-gang3 (李秀刚), REN Yuan-jie1 (任远. Reliability Prediction Method Based on State Space Model for Rolling Element Bearing[J]. 上海交通大学学报（英文版）, 2015, 20(3): 317-321.
[3]	WEN Cheng-yu* (文成玉), DONG Liang (董良), JIN Xin (金欣). Feature Extraction of Bearing Vibration Signals Using Second Generation Wavelet and Spline-Based Local Mean Decomposition[J]. 上海交通大学学报（英文版）, 2015, 20(1): 56-60.
[4]	SU Sai-sai1 (苏赛赛), CHEN Ke-wei1,2 (陈克非), HUANG Qiu1* (黄秋). Discriminant Analysis in the Study of Alzheimer’s Disease Using Feature Extractions and Support Vector Machines in Positron Emission Tomography with 18F-FDG [J]. 上海交通大学学报（英文版）, 2014, 19(5): 555-560.
[5]	ZHANG Zhi-fen* (张志芬), ZHONG Ji-yong (钟继勇), CHEN Yu-xi (陈玉喜), CHEN Shan-ben (陈善本). Feature Extraction and Modeling of Welding Quality Monitoring in Pulsed Gas Touch Argon Welding Based on Arc Voltage Signal[J]. 上海交通大学学报（英文版）, 2014, 19(1): 11-16.