摘要
智能水下机器人视觉识别系统的使命是快速、准确地处理获得水下目标的相关信息并及时反馈给计算机来指导机器人进行下一步的任务。为了在保证分割质量的前提下实现快速图像分割,结合梯度算子、图像的直方图特征和采样计算,并以图像的相对信息损耗为约束,提出了一种基于熵约束的快速FCM聚类水下图像分割算法,并依据水下图像分割效果和模糊划分的有效性评价指标,详尽研究了新算法中加权指数m的取值规律性。实验结果表明,这种算法能够获得较好的分割质量和时间效率,符合机器人对实时性的需求。
The mission of the vision system of autonomous underwater vehicle（AUV） is dealing with the information about the object in the complex environment rapidly and exactly for AUV to use the obtained result for the next task.So,aiming at realizing the image segmentation quickly on the precondition of high qualification,a fast fuzzy C-means algorithm based on entropy constraint for underwater image segmentation was proposed,in which the gradient operator,the histogram＇s statistical characterization,sampling-computation and the relative information loss were considered comprehensively,and regularity of taking value of fuzzy weighted exponent m in this new algorithm was studied in detail by use of underwater image segmentation result and appraisal index of validity of fuzzy partition.Experimental results show that the novel algorithm can get a better segmentation result and the time efficiency is improved and the request of highly real-time effectiveness of AUV is satisfied.
出处
《计算机科学》
CSCD
北大核心
2010年第12期243-246,286,共5页
Computer Science
基金
国家自然科学基金项目(50909025/E091002)
水下智能机器人技术国防重点实验室开放课题研究基金(2008003)资助
关键词
水下图像分割
AUV
相对信息损耗
模糊划分
加权指数
实时性
Underwater image segmentation
Autonomous underwater vehicle（AUV）
Relative information loss
Fuzzy partition
Weighted exponent
Real-time effectiveness
