Wavelet Moment Invariants Extraction of Underwater Laser Vision Image

doi:10.1007/s12204-013-1454-6

Abstract

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

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

CLC Number:

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]. Journal of shanghai Jiaotong University (Science), 2013, 18(6): 712-718.

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