基于2维Tsallis熵的水下图像目标检测

唐旭东; 庞永杰; 张铁栋; 李晔

基于2维Tsallis熵的水下图像目标检测

哈尔滨工程大学水下机器人技术国防科技重点实验室,黑龙江,哈尔滨,150001

详细信息

作者简介:
唐旭东(1983- )，男，博士生．研究领域：水下机器人智能控制，模式识别，管道跟踪．
庞永杰(1955- )，男，教授，博士生导师．研究领域：水下机器人设计，运动仿真．

通信作者:
唐旭东, tangxudong@yahoo.cn

中图分类号: TP242
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出版历程
- 收稿日期: 2009-07-11
- 修回日期: 2009-11-03
- 发布日期: 2010-05-14

Detection of Objects in Underwater Images Based on the Two-Dimensional Tsallis Entropy

National Key Laboratory of Science and Technology on Autonomous Underwater Vehicle, Harbin Engineering University, Harbin 150001, China

摘要

摘要: 针对传统图像检测方法在水下图像处理过程中存在目标区域定位不准确、目标细节丢失、目标形状变形的问题，文中利用Tsallis熵的非广延性，提出了一种基于边缘信息的2维直方图，并以最大2维Tsallis熵为准则，利用改进粒子群优化算法寻找最佳阈值．水下图像处理试验表明，该算法是一种有效的水下图像目标检测方法，与传统方法相比，具有更强的自适应性和鲁棒性．
- 水下图像 /
- Tsallis熵 /
- 目标检测 /
- 改进粒子群优化算法
Abstract: For the problems in underwater image processing by traditional image detection methods,such as inaccurate location of objects regions,loss of object details and distortion of object shape,etc.,a new two-dimensional histogram based on edge information is proposed by utilizing the non-extensive property of Tsallis entropy.The improved particle swarm optimization(PSO) is used to search the best threshold value by maximizing the two-dimensional Tsallis entropy.The test results of some underwater images show that it is efficient to detect objects in underwater images.Comparing with traditional methods,the proposed approach shows better adaptability and robustness.
- underwater image /
- Tsallis entropy /
- object detection /
- improved PSO(particle swarm optimization)

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参考文献(13)

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