摘要
针对应用二维经验模式分解算法进行水下图像边缘检测时需要人工设定检测阈值的问题,提出一种BEMD与ROC曲线分析相结合的自适应图像边缘检测新方法.首先通过BEMD算法将水下图像分解成多层内禀模式函数(IMF)分量图像,然后利用不同参数组合的Canny检测算子对IMF分量图像进行细化处理,生成各层IMF分量的二值化图像集,最后利用ROC曲线分析技术求得IMF分量图像的最佳检测阈值,从而确定了理想的BEMD边缘特征提取图.实验结果表明:该算法能够避免人工设置检测阈值带来的操作误差,可实现图像边缘特征提取检测阈值的自适应设定.水下图像处理实例验证了所提方法的正确性和有效性.
A novel method combining BEMD and receiver operating characteristics （ROC） curve is presented in this paper to solve the problem that the threshold is greatly affected by personal experience when underwater image edge detection is performed using a bi-dimensional empirical mode decomposition （BEMD） algorithm. Firstly, the BEMD algorithm is employed to decompose an underwater image into several intrinsic mode functions （IMFs） and a residual. Then several IMF images are computed using combinations of the Canny detector parameters, and the image binaryzation results are generated accordingly. The ideal BEMD edge feature extraction maps are estimated using correspondence threshold which is optimized by ROC analysis. The experimental results show that the proposed algorithm is able to avoid the operation error caused by manual setting of the detection threshold, and to adaptively set the image feature detection threshold. The proposed method has been proved to be accuracy and effectiveness by the underwater image processing examples.
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2013年第2期117-122,共6页
Journal of Harbin Institute of Technology
基金
国家公益性行业科研专项(201003024)
辽宁省教育厅科研项目(LS2010046)
关键词
水下图像
二维经验模式分解
ROC曲线分析
边缘检测
underwater image
hi-dimensional empirical mode decomposition
receiver operating characteristics curve
edge feature detector
