摘要
由于水下图像受到水下光照条件以及水质的一些特性影响,存在对比度低、灰度不均、目标边缘模糊等特点。传统基于最大熵原理的阈值法尽管能实现某些特定的分割任务,但是采用凸模糊集的隶属函数和穷举法存在计算复杂度高、时效性差等缺点。在传统模糊熵分割算法的基础上,重新定义了模糊熵,并根据最大熵原理,利用改进粒子群优化算法(PSO)来搜索分割阈值。通过水下图像处理试验证明,该算法对简单背景的图像分割是有效的;与传统分割方法相比,具有更强的自适应性和抗噪性。
Under the influence of lighting condition and water quality,the underwater images have low contrast,uneven gray scales and fuzzy edge of objects.Though tradition threshold methods based on maximum entropy principle could divide the image into objects and background,its complex and time-consuming computation by flange-fuzzy membership function and method of exhaustion is often an obstacle.In this paper,based on the traditional threshold methods,fuzzy entropy is redefined on given images,and the improved PSO is used to search the optimal threshold based on maximum entropy principle.The experiments prove that this novel approach is effective for simple back-ground underwater images.Comparing with the traditional methods,the new method shows better adaptability and noise restraining performance.
出处
《海洋工程》
CSCD
北大核心
2010年第2期128-133,共6页
The Ocean Engineering
基金
国家"863"计划资助项目(2008AA092301)
中国博士后科学基金资助项目(20080440838)
黑龙江省博士后资助项目
哈尔滨工程大学基础研究基金资助(HEUFT08001
HEUFT08017)
水下智能机器人技术国防科技重点实验室开放课题研究基金资助(2007001
2008003)
