Object Auto-Recognition for Underwater Targets
The affine invariants is constructed based on region moments in order to eliminate the negative effects, which are brought by the underwater images under the influence of the lighting condition and some character of water media. Aiming at the draw backs of traditional BP neural network, such as converging slowly and tending to get into the local minimize, a new method of designing BP neural net works based on immune genetic algorithm (IGA) is proposed. The mechanisms of diversity maintaining and antibody density regulation exhibited in a biological immune system are introduced into IGA based on genetic algorithm (GA). The proposed algorithm overcome the problems of GA on search efficiency, individual diversity and premature, and enhanced the convergent performance effectively. The affine invariant features of four different objects are extracted and selected as the input of the trained neural network. The feasibility and advantages of this method are demonstrated by the experimental results.
Underwater image Feature eztraction neural network Immune genetic algorithm
Tang Xu-dong Pang Yong-jie Li Ye Zhang He
Key lab of autonomous underwater vehicle, ,Harbin Engineering University, Harbin 150001, China
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
4612-4616
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）