Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels
Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels
复制标题
基于自动各向异性高斯核的抗噪图像边缘检测
DOI：
10.1016/j.patcog.2016.10.008
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发表时间：
2017-03-01
影响因子：
8
通讯作者：
Chen, Long
中科院分区：
文献类型：
--
作者：
Zhang, WeiChuan;Zhao, YaLi;Chen, Long
提出了一种基于自动各向异性高斯核函数（ANGKs）的噪声鲁棒边缘检测算法，解决了Canny边缘检测算法容易丢失明显交叉边缘的问题。首先从噪声抑制、边缘分辨和定位精度三个方面设计了自动ANGK，协调了它们之间的矛盾。其次，分析了各向同性高斯核Canny检测结果中交叉边缘点缺失的原因。第三，利用自动ANGK对图像进行平滑处理，并采用一种改进的边缘提取方法进行边缘提取。最后，使用总测试接收器操作特征（ROC）曲线和普拉特品质因数（FOM）来评估所提出的检测器与最先进的边缘检测器的比较。实验结果表明，该算法对无噪图像和含噪图像均能取得较好的效果。
This paper presents a novel noise robust edge detector based upon the automatic anisotropic Gaussian kernels (ANGKs), which also addresses the current problem that the seminal Canny edge detector may miss some obvious crossing edge details. Firstly, automatic ANGKs are designed according to the noise suppression, edge resolution and localization precision, which also conciliate the conflict between them. Secondly, reasons why cross-edge points are missing from Canny detector results using isotropic Gaussian kernel are analyzed. Thirdly, the automatic ANGKs are used to smooth image and a revised edge extraction method is used to extract edges. Finally, the aggregate test receiver-operating-characteristic (ROC) curves and Pratt's Figure of Merit (FOM) are used to evaluate the proposed detector against state-of-the-art edge detectors. The experiment results show that the proposed algorithm can obtain better performance for noise-free and noisy images.